Patent Publication Number: US-2007123472-A1

Title: Filtering predictive data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
      The present application is related to and claims the benefit of the earliest available effective filing date(s) from the following listed application(s) (the “Related Applications”) (e.g., claims earliest available priority dates for other than provisional patent applications or claims benefits under 35 USC § 119(e) for provisional patent applications, for any and all parent, grandparent, great-grandparent, etc. applications of the Related Application(s)).  
      Related Applications:  
     
         
          1. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Data Techniques Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord, and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/222,031, filed Sep. 8, 2005.  
          2. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Data Techniques Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord, and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/241,868, filed Sep. 30, 2005.  
          3. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Accessing Data Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord, and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/262,499, filed Oct. 28, 2005.  
          4. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Accessing Data Related to Tissue Coding, naming Edward K. Y. Jung, Robert W. Lord, and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/286,133, filed Nov. 23, 2005.  
          5. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Accessing Predictive Data, naming Edward K. Y. Jung, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo Jr., and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/311,906, filed Dec. 19, 2005.  
          6. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Accessing Predictive Data, naming Edward K. Y. Jung, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo Jr., and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. 11/314,730, filed Dec. 21, 2005.  
          7. For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation in part of currently co-pending United States patent application entitled Accessing Predictive Data, naming Edward K. Y. Jung, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo Jr., and Lowell L. Wood, Jr., as inventors, U.S. Ser. No. To be Assigned, filed Jan. 31, 2006. 
       
    
    
    
      The United States Patent Office (USPTO) has published a notice to the effect that the USPTO&#39;s computer programs require that patent applicants reference both a serial number and indicate whether an application is a continuation or continuation-in-part. Stephen G. Kunin,  Benefit of Prior - Filed Application , USPTO Official Gazette Mar. 18, 2003, available at http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm. The present applicant entity has provided above a specific reference to the application(s)from which priority is being claimed as recited by statute. Applicant entity understands that the statute is unambiguous in its specific reference language and does not require either a serial number or any characterization, such as “continuation” or “continuation-in-part,” for claiming priority to U.S. patent applications. Notwithstanding the foregoing, applicant entity understands that the USPTO&#39;s computer programs have certain data entry requirements, and hence applicant entity is designating the present application as a continuation-in-part of its parent applications as set forth above, but expressly points out that such designations are not to be construed in any way as any type of commentary and/or admission as to whether or not the present application contains any new matter in addition to the matter of its parent application(s).  
      All subject matter of the Related Applications and of any and all parent, grandparent, great-grandparent, etc. applications of the Related Applications is incorporated herein by reference to the extent that such subject matter is not inconsistent herewith.  
     Technical Field  
      This description relates to data handling techniques.  
     SUMMARY  
      An embodiment provides a method. In one implementation, the method includes but is not limited to accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, determining a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, applying a filter criteria to the at least one dataset to obtain a filtered dataset, and determining a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a computer program product. In one implementation, the computer program product includes but is not limited to a signal-bearing medium bearing at least one of one or more instructions for accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, the signal bearing medium bearing one or more instructions for determining a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, the signal bearing medium bearing one or more instructions for applying a filter criteria to the at least one dataset to obtain a filtered dataset, and the signal bearing medium bearing one or more instructions for determining a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a system. In one implementation, the system includes but is not limited to a computing device and instructions. The instructions when executed on the computing device cause the computing device to access at least one dataset, based on at least one treatment parameter and at least one predictive basis, determine a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, apply a filter criteria to the at least one dataset to obtain a filtered dataset, and determine a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a device. In one implementation, the device includes but is not limited to a treatment system, the treatment system includes but is not limited to a treatment data memory that is operable to store treatment data in association with at least one predictive basis, and treatment logic that is operable to access, from the treatment data memory and based on at least one treatment parameter and the at least one predictive basis, at least one dataset associated with the at least one predictive basis, the treatment logic being operable to determine a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, and to determine a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on a filtered dataset obtained by application of a filter criteria to the at least one dataset. In addition to the foregoing, other device aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a method. In one implementation, the method includes but is not limited to accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, determining data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, applying a filter criteria to the at least one dataset to obtain a filtered dataset, and determining data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a computer program product. In one implementation, the computer program product includes but is not limited to a signal-bearing medium bearing at least one of one or more instructions for accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, the signal bearing medium bearing one or more instructions for determining data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, the signal bearing medium bearing one or more instructions for applying a filter criteria to the at least one dataset to obtain a filtered dataset, and the signal bearing medium bearing one or more instructions for determining data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a system. In one implementation, the system includes but is not limited to a computing device and instructions. The instructions when executed on the computing device cause the computing device to access at least one dataset, based on at least one treatment parameter and at least one predictive basis, determine data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, apply a filter criteria to the at least one dataset to obtain a filtered dataset, and determine data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      An embodiment provides a device. In one implementation, the device includes but is not limited to a treatment system, the treatment system includes but is not limited to a treatment data memory that is operable to store treatment data in association with at least one predictive basis, and treatment logic that is operable to access, from the treatment data memory and based on at least one treatment parameter and the at least one predictive basis, at least one dataset associated with the at least one predictive basis, the treatment logic being operable to determine data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, and to determine data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on a filtered dataset obtained by application of a filter criteria to the at least one dataset. In addition to the foregoing, other device aspects are described in the claims, drawings, and text forming a part of the present disclosure.  
      In addition to the foregoing, various other embodiments are set forth and described in the text (e.g., claims and/or detailed description) and/or drawings of the present description.  
      The foregoing is a summary and thus contains, by necessity, simplifications, generalizations and omissions of detail; consequently, those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, features, and advantages of the devices and/or processes described herein, as defined by the claims, will become apparent in the detailed description set forth herein. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  illustrates an example clinical system in which embodiments may be implemented, perhaps in a device.  
       FIG. 2  illustrates certain alternative embodiments of the clinical system of  FIG. 1 .  
       FIG. 3  illustrates an alternative embodiment of treatment data associated with the clinical system of  FIG. 1 .  
       FIG. 4  illustrates another alternative embodiment of treatment data associated with the clinical system of  FIG. 1 .  
       FIG. 5  illustrates another alternative embodiment of treatment data associated with the clinical system of  FIG. 1 , with specific examples of treatment data.  
       FIG. 6  illustrates additional alternative embodiments of treatment data associated with the clinical system of  FIG. 1 , with specific examples of treatment data.  
       FIG. 7  illustrates additional alternative embodiments of treatment data associated with the clinical system of  FIG. 1 , with specific examples of treatment data.  
       FIG. 8  illustrates an example screenshot of a graphical user interface for filtering predictive data.  
       FIG. 9  illustrates an alternative embodiment of the clinical system of  FIG. 1  in which the clinical system is configured to provide access to predictive data.  
       FIG. 10  illustrates an operational flow representing example operations related to filtering predictive data.  
       FIG. 11  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 12  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 13  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 14  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 15  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 16  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 17  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 18  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 19  illustrates an alternative embodiment of the example operational flow of  FIG. 10 .  
       FIG. 20  illustrates a partial view of an example computer program product that includes a computer program for executing a computer process on a computing device.  
       FIG. 21  illustrates an example system in which embodiments may be implemented.  
       FIG. 22  illustrates an operational flow representing example operations related to accessing predictive data.  
       FIG. 23  illustrates an alternative embodiment of the example operational flow of  FIG. 22 .  
       FIG. 24  illustrates an alternative embodiment of the example operational flow of  FIG. 22 .  
       FIG. 25  illustrates an alternative embodiment of the example operational flow of  FIG. 22 .  
       FIG. 26  illustrates a partial view of an example computer program product that includes a computer program for executing a computer process on a computing device.  
       FIG. 27  illustrates an example system in which embodiments may be implemented. 
    
    
      The use of the same symbols in different drawings typically indicates similar or identical items.  
     DETAILED DESCRIPTION  
       FIG. 1  illustrates an example clinical system  100  in which embodiments may be implemented. The clinical system  100  includes a treatment system  102 . The treatment system  102  may be used, for example, to store, recall, access, process, implement, or otherwise use information that is beneficial in a clinical setting(s). For example, the treatment system  102  may be used to diagnose or treat patients by storing and/or providing information as to whether or how treatment agent(s) may be applied to a specific region(s) of interest of the human body, such as, for example, a lobe of the lungs, breast tissue, cancerous tissue at a certain bodily location, or other such regions of interest. As a further example, the treatment system  102  may provide information as to whether and/or how to minimize or avoid application of such treatment agents to regions of non-interest (for example, regions to which the treatment agent(s) should not be applied, in order to avoid, e.g., problematic side effects and other undesired results). On the basis of such clinical information, for example, targeted applications of treatment agents (e.g., medication, imaging agents, or other beneficial medical agents) may be carried out in a manner that achieves a desired outcome, while minimizing or eliminating unwanted applications to non-targeted bodily regions.  
      In  FIG. 1 , the treatment system  102  is used by a clinician  104 . The clinician  104  may, for example, use the treatment system  102  to enter, store, request, or access clinical information such as, for example, the various examples provided herein. The clinician  104  may generally represent, for example, any person involved in health care, including, for example, a doctor, a nurse, a physician&#39;s assistant, or a medical researcher. The clinician  104  also may represent someone who is involved in health care in the sense of developing, managing, or implementing the treatment system  102 , e.g., a software developer with clinical knowledge (or access to clinical knowledge), a database manager, or an information technologies specialist. Even more generally, some or all of various functions or aspects described herein with respect to the clinician  104  may be performed automatically, e.g., by an appropriately-designed and implemented computing device, or by software agents or other automated techniques.  
      A patient  106  generally represents any person with an illness, injury, or disease, or who is thought to potentially have such an illness, injury, or disease, or who may be wholly or partially healthy but who is nonetheless studied in order to determine information about such an illness, injury, or disease. The patient  106  also may represent or include other diagnostic and/or animal subjects that may be used in order, for example, to determine an efficacy of a particular medication or treatment, specific examples of which are provided herein. The patient  106  may represent a particular patient in a given clinical setting, such as in a doctor&#39;s office, or in a hospital, who is to be diagnosed and/or treated using the treatment system  102 . The patient  106  also may represent the more abstract notion of a class of patients (e.g., patients having a certain age, gender, race, genetic makeup, or disposition to illness or disease), or, even more generally, may represent the general notion of a generic patient during basic research and/or development or application of various medical treatments or procedures. In this latter sense, the patient  106  may also represent a non-human animal (such as a primate) believed to be sufficiently similar to a human for the particular purposes that they may usefully substitute for such for the particular purposes.  
      As such, the patient  106  generally possesses or is associated with, for example, some or all of the various organs, systems, organ systems, organ subsystems, diseased tissue, and/or healthy tissue that may be found in the body. In  FIG. 1 , the patient  106  is illustrated as having a lung  108  and a pancreas  110 , so that these (and other) body parts may be used as the bases for the specific examples given herein. Of course, many other applications of the treatment system  102  exist, over and above the examples provided herein.  
      In an exploded portion  108   a  of the lung  108 , various example elements are illustrated, although not drawn to scale for the purposes of clarity and ease of illustration and description. For example, the lung  108  may include a healthy tissue portion  112 , and a diseased tissue portion  114 . The healthy tissue  112  may include, for example, healthy lung tissue, while the diseased tissue  114  may include, for example, a tumor or other cancerous tissue.  
      The lung  108  also may include a blood vessel  116 , which is illustrated in a cut-away view, and which includes a tissue component  118  known as, by way of example nomenclature, the endothelium, endothelial layer, or endothelial cells. The endothelium or endothelial layer  118  generally refers to a layer of cells that lines an interior of a portion of the circulatory system, such as the blood vessel  116 . In  FIG. 1 , the blood vessel  116  and the endothelial layer  118  are illustrated as being in the vicinity of the diseased tissue  114 . In contrast, an example of a blood vessel  120  is illustrated that contains endothelial layer  122 . The blood vessel  120  is shown as being in the vicinity of the healthy tissue  112  of the lung  108 .  
      Certain properties of the endothelial layer  118  and the endothelial layer  122  may enable the targeted delivery of one or more treatment agents to a vicinity of the diseased tissue  114  and the healthy tissue  112 , respectively. For example, blood (and other cells contained therein) will be transported within and along a length of the blood vessel  116 , where the length of the blood vessel  116  naturally extends a relatively long distance in either direction toward/away from the diseased tissue  114 . However, cells of the endothelial layer  118  that have developed and/or grown over a period of time in a vicinity of the diseased tissue  114  may exhibit characteristics that are unique, or essentially unique, to a site on the endothelial layer  118  in that particular vicinity.  
      For example, the diseased tissue  114  may include a tumor that has grown over a period of time. During that period of time, a corresponding growth or development of a site on the endothelial layer  118  may reflect, or otherwise be correlated with and/or affected by, the growth of the diseased tissue (tumor)  114 . This correlation between the history or ancestry of the site on the endothelial layer  118  in the vicinity of the diseased tissue  114  may result in unique, or almost unique, properties of the tissue ancestry-correlated site, such as, for example, a display of specific and identifiable proteins. Moreover, similar comments may apply to a tissue ancestry-correlated site along the endothelial layer  122  of the blood vessel  120 , in the vicinity of the healthy tissue  112 . In this way, each such tissue ancestry-correlated site, whether in the lung or in other sites in the body, may be used to provide, effectively, a molecular-level address that specifies a location within the body, e.g., a location of the diseased tissue  114  and/or the healthy tissue  112 .  
      Other mechanisms exist by which such effective molecular-level addresses, such as those that may, in some instances, entail some logical relation to tissue ancestry-correlated sites, may arise at a given location in the body. For example, such sites may originate in or at a first location in the body, and may thereafter undergo transport to, and engraftment/implantation at, a second location in the body. For example, tissue may originate in bone marrow, or in a distant neoplasm, and may be transported through the vasculature to another, second location in the body (e.g., the lungs  108 ). Such tissue, which may be, for example, as small as a single cell, may embed at the second location and thereafter serve as a molecular-level address or site to which other agent(s) may bind.  
      Accordingly, such tissue ancestry-correlated sites may be used to direct treatment agents (such as, for example, medications, imaging agents, or radio-immunotherapy agents) in a desired fashion. For example, as described in more detail in certain examples provided herein, radionuclides may be applied to the diseased tissue  114 .  
      In this regard, it should be understood that, without use of the tissue ancestry-correlated site(s) described herein, it may be difficult to direct such treatment agents to desired body regions with a necessary or desired level of precision. For example, many treatment agents may be delivered by injection (or by other delivery modalities, e.g., swallowing or absorption through the skin) into a bloodstream of the patient  106 . However, without an effective way to direct the treatment agents once in the bloodstream, a positive impact of the treatment agents may be reduced or eliminated. Moreover, ancillary delivery of the treatment agents to undesired regions (e.g., delivery of radionuclides to the healthy tissue  112  and/or to the pancreas  110  or other organs) may result in harm to the patient  106 . Such harm may be particularly acute or problematic in cases where, for example, a concentration, dosage, or amount of the treatment agent in the bloodstream is required to be increased relative to an optimal treatment amount, simply to ensure that some portion of the treatment agent reaches and affects a desired end target. Similar comments may apply to other treatment modalities. For example, treatment of the diseased tissue  114  (e.g., a tumor) may be performed by radiation therapy in which the patient is exposed to radiation, and, again, the net effect of such treatment(s) may be negative due to harm caused by the radiation to the healthy tissue  112 .  
      As just described, then, tissue ancestry-correlated sites may exist within and along the endothelial layers  118  and/or  122 , in the vicinity of correlated tissues that may serve as target(s) (e.g., the diseased tissue  114 ) for certain treatment agent(s). For example, these target-related tissue ancestry-correlated sites may include, as described herein, certain proteins that may be known to bind to/with certain other agents. In one specific example discussed herein, a target-related tissue ancestry-correlated binding site includes a protein, aminopeptidase-P (APP), that is known to bind with an agent such as, for example, I-labeled monoclonal antibodies. If a treatment agent (such as, for example, radionuclides) is associated with the target-related tissue ancestry-correlated binding agent (e.g., the I-labeled monoclonal antibodies), then injection of the target-related tissue ancestry-correlated binding agent into the bloodstream will result in delivery of the treatment agent (e.g., radionuclides) to the target-related tissue ancestry-correlated binding site (e.g., APP in the vicinity of the lung  108 ). That is, as the target-related tissue ancestry-correlated binding agent moves through the bloodstream, the target-related tissue ancestry-correlated binding agent will bind with the target-related tissue ancestry-correlated binding site in the vicinity of the, in this example, diseased tissue  114 , thus resulting in effective application of the attached treatment agent in the desired region of the body of the patient  106 .  
      In many cases, delivery of the treatment agent(s) to the vicinity of desired body regions, by delivering the treatment agents to defined sites along a blood vessel wall(s) in the desired vicinity, may be sufficient to obtain a desired result, even if the treatment agents are continually contained within the blood vessel(s) at the target-related tissue ancestry-correlated binding sites. In various cases, treatment agent delivery should occur with greater or lesser levels of specificity and/or efficacy. For example, in some cases, it may be sufficient to provide the treatment agent in the lung  108 , while in other cases the treatment agent must or should be applied substantially only to the diseased tissue  114 .  
      Additionally, in some cases, it may be possible and/or desirable to breach or penetrate a wall of the blood vessel(s)  116 / 120 , in order to reach associated tissue(s) directly. For example, in  FIG. 1 , an enlarged view  118   a  of the endothelial layer  118  is illustrated that includes a mechanism by which the treatment agents may directly access a direct end target of tissue (e.g., the diseased tissue  114 ). Specifically,  FIG. 1  illustrates a mechanism  124  that may include, for example, structures known as caveolae. Although the mechanism (e.g., caveolae)  124  are shown conceptually in  FIG. 1  as tubes or access points, caveolae generally refer to small invaginations of a surface of the blood vessel  116  that carry out certain transport and/or signaling functions between cells within the blood vessel  116  and cells outside of the blood vessel  116  (e.g., the diseased tissue  114 ). Further discussion regarding caveolae  124  is provided in various examples, herein.  
      Although many other examples are provided herein and with reference to the various figures, it should be understood that many types and instances of treatment data may play a role in the use and application of the various concepts referenced above and described in more detail herein. The treatment system  102  may store such treatment data  126  in a database or other memory, for easy, convenient, and effective access by the clinician  104 .  
      The treatment data  126  may include, for example, not only the target-related tissue ancestry-correlated binding site(s) and/or the related target-related tissue ancestry-correlated binding agent(s), but also various other parameters and/or characteristics related to treatment of the patient  106 , examples of which are provided herein. Through detailed storage, organization, and use of the treatment data  126 , the clinician  104  may be assisted in determining optimal treatment techniques for the patient  106 , in order, for example, to select and deliver an appropriate type and/or level of a treatment agent, with an appropriate degree of accuracy, to a desired end target (based on an appropriate target-related tissue ancestry-correlated binding site and/or an appropriate target-related tissue ancestry-correlated binding agent), while minimizing any negative impact of such a selection/delivery, if any, on other regions of the body of the patient  106 . Ordered assignment and/or storage of information within the treatment data  126 , as described herein, facilitates and/or enables such recall, access, and/or use of the treatment data by the clinician  104  in treating the patient  106 .  
      In the treatment system  102 , treatment logic  128  may be used to store, organize, access, recall, or otherwise use the information stored in the treatment data  126 . For example, the treatment logic  128  may access a database management system (DBMS) engine  130 , which may be operable to perform computing operations to insert or modify new data into/within the treatment data  126 , perhaps in response to new research or findings, or in response to a preference of the clinician  104 . For example, if a new treatment agent is discovered to be effective on the diseased tissue  114 , the clinician  104  may access the treatment system  102  using a user interface  132 , in order to use the DBMS engine  130  to associate the new treatment agent with one or more instances of the target-related tissue ancestry-correlated binding site(s) and/or target-related tissue ancestry-correlated binding agent(s) that may be known to be useful in targeting the diseased tissue  114 , within the treatment data database  126  (assuming that the treatment agent is suitable for direct or indirect delivery via the target-related tissue ancestry-correlated binding agent, as described herein). As another example, if a new target-related tissue ancestry-correlated binding site is identified in the endothelial layer  118  in the vicinity of the diseased tissue  114 , then this new target-related tissue ancestry-correlated binding site may be associated with one or more instances of a target-related tissue ancestry-correlated binding agent, e.g., there may be more than one agent that is useful in attaching to the new target-related tissue ancestry-correlated binding site for delivery of one or more treatment agents.  
      Similarly, in a case where the clinician  104  seeks, for example, to diagnose or treat the patient  106 , the clinician  104  may access the user interface  132  to use the treatment logic  128  and/or the DBMS Engine  130  to determine best known methods or treatments to be applied in a given clinical scenario. For example, if the patient  106  has a certain type of disease or illness in a certain region of the body, then the clinician may input this information via the user interface  132  in order to obtain one or more options for treating the disease or illness. For example, if the patient  106  exhibits the diseased tissue  114 , then the clinician  104  may select the (type of) diseased tissue  114  in the lung  108  as an end target, and the treatment logic  128  may then interface with the DBMS engine  130  to obtain, from the treatment data  126 , one or more options for providing the treatment agent to the diseased tissue  114 , e.g., one or more target-related tissue ancestry-correlated binding sites (such as, for example, two different proteins that are expressed or displayed in the endothelial layer  118  in the vicinity of the diseased tissue  114 ). As another example, if the clinician  104  is already aware of a target-related tissue ancestry-correlated binding site in the vicinity of the diseased tissue  114 , then the clinician  104  may input this information into the treatment system  102  and be provided with one or more, for example, target-related tissue ancestry-correlated binding agents that may be known to attach to the known target-related tissue ancestry-correlated binding site.  
      In this regard, it should be understood that multiple instances of a target-related tissue ancestry-correlated binding site, as described, may be present at any one location in the body, and, moreover, virtually any region or site in the body having a blood-tissue interface may also exhibit an associated, target-related tissue ancestry-correlated binding site. Further, new instances of target-related tissue ancestry-correlated binding sites may be discovered and/or approved for clinical use on a relatively frequent basis. Still further, there may be many different treatment parameters and/or characteristics that may be related to the various target-related tissue ancestry-correlated binding site(s) and/or target-related tissue ancestry-correlated binding agent(s), such as, for example, treatment agents and/or delivery mechanisms.  
      As a result, the clinician  104 , e.g., a physician in the field, may not be aware of all currently-available content of the treatment data  126 . Thus, the treatment system  102  provides the clinician with readily-available, accurate, current, and/or comprehensive treatment information, and also provides techniques to ensure that the treatment information remains accurate, current, and/or comprehensive, by allowing the addition and/or modification of the existing treatment data  126 , as new treatment information becomes available.  
      In  FIG. 1 , the treatment system  102  is illustrated as possibly being included within a device  134 . The device  134  may include, for example, a mobile computing device, such as a personal digital assistant (PDA), or a laptop computer. Of course, virtually any other computing device may be used to implement the treatment system  102 , such as, for example, a workstation, a desktop computer, or a tablet PC.  
      Additionally, not all of the treatment system  102  need be implemented on a single computing device. For example, the treatment data  126  may be stored on a remote computer, while the user interface  132  and/or treatment logic  128  are implemented on a local computer. Further, aspects of the treatment system  102  may be implemented in different combinations and implementations than that shown in  FIG. 1 . For example, functionality of the DBMS engine  130  may be incorporated into the treatment logic  128  and/or the treatment data  126 .  
      The treatment data  126  may be stored in virtually any type of memory that is able to store and/or provide access to information in, for example, a one-to-many, many-to-one, and/or many-to-many relationship. Such a memory may include, for example, a relational database and/or an object-oriented database, examples of which are provided in more detail herein.  
       FIG. 2  illustrates certain alternative embodiments of the clinical system  100  of  FIG. 1 . In  FIG. 2 , the clinician  104  uses the user interface  132  to interact with the treatment system  102  deployed on the clinician device  134 . The clinician device  134  is in communication over a network  202  with a data management system  204 , which is also running the treatment system  102 ; the data management system  204  may be interacted with by a data manager  206  through a user interface  208 . Of course, it should be understood that there may be many clinicians other then the specifically-illustrated clinician  104 , each with access to an individual implementation of the treatment system  102 . Similarly, multiple data management systems  204  may be implemented.  
      In this way, the clinician  104 , who may be operating in the field, e.g., in an office and/or hospital environment, may be relieved of a responsibility to update or manage contents in the treatment data  126 , or other aspects of the treatment system  102 . For example, the data management system  204  may be a centralized system that manages a central database of the treatment data  126 , and/or that deploys or supplies updated information from such a central database to the clinician device  134 .  
       FIG. 3  illustrates an alternative embodiment of the treatment data  126  associated with the clinical system  100  of  FIG. 1 . In  FIG. 3 , and in the various examples herein, a particular nomenclature is used for the terms described above and related terms, in order to provide consistency and clarity of description. However, it should be understood that other terminology may be used to refer to the same or similar concepts.  
      In  FIG. 3 , treatment parameters  302  are stored and organized with respect to a plurality of treatment characteristics  304 . The treatment characteristics  304  include many of the terms and concepts just described, as well as additional, but not exhaustive, terms and concepts that may be relevant to a use and operation of the treatment system  102 .  
      For example, the treatment characteristics  304  include a direct end target  306 . The direct end target  306  may refer, for example, to any tissue, organ, organ system, organ subsystem (or type thereof), or any other body part or region that may be targeted for healing, destruction, repair, enhancement, and/or imaging that may be targeted—directly or indirectly—via an associated target-related tissue ancestry-correlated binding site  314  and/or an associated target-related tissue ancestry-correlated binding agent  316  and/or an associated treatment agent delivery mechanism relative to the target-related tissue ancestry-correlated binding agent  318  and/or an associated treatment agent  320 . A discriminated end target  308  refers to targets that should be avoided during implementation of the healing, destruction, repair, enhancement and/or imaging actions that may be discriminated—directly or indirectly—via an associated target-related tissue ancestry-correlated binding site  314  and/or an associated target-related tissue ancestry-correlated binding agent  316  and/or an associated treatment agent delivery mechanism relative to the target-related tissue ancestry-correlated binding agent  318  and/or an associated treatment agent  320 . For example, in  FIG. 1 , the lung  108  may include the direct end target  306  as the diseased tissue  114 , and may include the discriminated end target  308  as the healthy tissue  112 , and/or the pancreas  110 .  
      Somewhat analogously, a direct intermediate target  310  refers to targets that are connected to, associated with, or in the vicinity of the direct end target that may be targeted via an associated target-related tissue ancestry-correlated binding site  314  and/or an associated target-related tissue ancestry-correlated binding agent  316  and/or an associated treatment agent delivery mechanism relative to the target-related tissue ancestry-correlated binding agent  318  and/or an associated treatment agent  320 . For example, a portion of the endothelial layer  118  in a vicinity of the diseased tissue  114  (or other end target) may act as a direct intermediate target  310 . Then, a discriminated intermediate target  312  may refer to endothelial tissue of the layer  118  that is not in a vicinity of the diseased tissue  114  that may be discriminated via an associated target-related tissue ancestry-correlated binding site  314  and/or an associated target-related tissue ancestry-correlated binding agent  316  and/or an associated treatment agent delivery mechanism relative to the target-related tissue ancestry-correlated binding agent  318  and/or an associated treatment agent  320 .  
      As already referenced, a target-related tissue ancestry-correlated binding site  314  refers to a determined chemical and/or genetic and/or biological structure to which various chemical compounds and/or genes may be affixed. For example, the target-related tissue ancestry-correlated binding site  314  may include a specific protein that is displayed at the endothelial layer  118  in a vicinity of the diseased tissue  114 . The target-related tissue ancestry-correlated binding site  314  may be selectively associated with the direct end target  306  either directly or through the direct intermediate target  310 .  
      A target-related tissue ancestry-correlated binding agent  316 , then, may refer to some specific chemical and/or genetic and/or biological structure that more or less selectively binds or attaches to a related one of the target-related tissue ancestry-correlated binding sites  314 . The target-related tissue ancestry-correlated binding agent  316  also may be associated with a treatment agent delivery mechanism relative to the target-related tissue ancestry-correlated binding agent  318 , which may refer either to something that may be directly attached to (or associated with) the target-related tissue ancestry-correlated binding agent  316 , and/or something that may be attached to (or associated with) one or more intermediary or indirect structures that attach to the target-related tissue ancestry-correlated binding agent  316  and that act to house and/or deliver a treatment agent  320 . As an example of the intermediary or indirect structures just referenced, a nano-container may be used that dissolves and/or otherwise opens in a vicinity of the target-related tissue ancestry-correlated binding site  314 , and thereby releases and/or delivers the treatment agent  320  included inside.  
      The treatment agent  320  thus binds/attaches to, or otherwise is associated with, either directly or indirectly, the target-related tissue ancestry-correlated binding agent  316 . Thus, as described, the treatment agent  320  may be effectively transported to the appropriate direct intermediate target  310  and thereby to the target-related tissue ancestry-correlated binding site  314 . In this way, the treatment agent  320  may be delivered to the direct end target  306  (or at least to a vicinity of the direct end target  306 ), while not being delivered either to the discriminated intermediate target(s)  312  and/or the discriminated end target(s)  308 .  
       FIG. 3  thus illustrates that there may be many different relationships or associations between any one (or more) of the treatment characteristics  304 . For example, one or more instances of any one or more of the treatment characteristics  304  may be considered to be one of the treatment parameters  302 , and thereafter associated with one or more instances of the remaining treatment characteristics  304 . For example, the direct end target  306  may be considered to be the treatment parameter(s)  302 , where a first instance  302   a  of the direct end target  306  may refer to diseased lung tissue, and the second instance  302   b  may refer to diseased breast tissue, and both instances may be associated with an instance of the target-related tissue ancestry-correlated binding agent  316 . Similarly, two or more instances of the target-related tissue ancestry-correlated binding agent  316  (e.g., I-labeled APP monoclonal antibodies targeted on two different antigens) may be associated with one treatment agent  320  (e.g., radio-immunotherapy via application of low levels of radionuclides).  
      Many other examples of relationships and associations between the various treatment parameters  302  and/or the treatment characteristics  304  (and/or other treatment information) may be defined or determined and stored in the treatment data  126  according to the treatment logic  128 . Certain of these examples are provided herein.  
      Additionally, although the treatment data  126  is illustrated conceptually in  FIG. 3  as a flat table in which one or more of the selected treatment parameters  302  are associated with one or more of the treatment characteristics, it should be understood that this illustration is for explanation and example only, and is not intended to be limiting in any way with respect to the various ways in which the treatment data  126  may be stored, organized, accessed, recalled, or otherwise used.  
      For example, the treatment data  126  may be organized into one or more relational databases. In this case, for example, the treatment data  126  may be stored in one or more tables, and the tables may be joined and/or cross-referenced in order to allow efficient access to the information contained therein. Thus, the treatment parameter(s)  302  may define a record of the database(s) that is associated with various ones of the treatment characteristics  304 .  
      In such cases, the various tables may be normalized so as, for example, to reduce or eliminate data anomalies. For example, the tables may be normalized to avoid update anomalies (in which the same information would need to be changed in multiple records, and which may be particularly problematic when treatment data database  126  is large), deletion anomalies (in which deletion of a desired field or datum necessarily but undesirably results in deletion of a related datum), and/or insertion anomalies (in which insertion of a row in a table creates an inconsistency with another row(s)). During normalization, an overall schema of the database may be analyzed to determine issues such as, for example, the various anomalies just referenced, and then the schema is decomposed into smaller, related schemas that do not have such anomalies or other faults. Such normalization processes may be dependent on, for example, desired schema(s) or relations between the treatment parameters  302  and/or treatment characteristics  304 , and/or on desired uses of the treatment data  126 .  
      Uniqueness of any one record in a relational database holding the treatment data  126  may be ensured by providing or selecting a column of each table that has a unique value within the relational database as a whole. Such unique values may be known as primary keys. These primary keys serve not only as the basis for ensuring uniqueness of each row (e.g., treatment parameter) in the database, but also as the basis for relating or associating the various tables within one another. In the latter regard, when a field in one of the relational tables matches a primary key in another relational table, then the field may be referred to a foreign key, and such a foreign key may be used to match, join, or otherwise associate (aspects of) the two or more related tables.  
       FIG. 3  and associated potential relational databases represent only one example of how the treatment data may be stored, organized, processed, accessed, recalled, and/or otherwise used.  
       FIG. 4  illustrates another alternative embodiment of treatment data  126  associated with the clinical system  100  of  FIG. 1 , in which the treatment data  126  is conceptually illustrated as being stored in an object-oriented database.  
      In such an object-oriented database, the various treatment parameter(s)  302  and/or treatment characteristic(s)  304 , and/or instances thereof, may be related to one another using, for example, links or pointers to one another.  FIG. 4  illustrates a conceptualization of such a database structure in which the various types of treatment data are interconnected, and is not necessarily intended to represent an actual implementation of an organization of the treatment data  126 .  
      The concepts described above may be implemented in the context of the object-oriented database of  FIG. 4 . For example, two instances  320   a  and  320   b  of the treatment agent  320  may be associated with one (or more) instance  316   a  of the target-related tissue ancestry-correlated binding agent  316 . Meanwhile, two instances  316   a  and  316   b  of the target-related tissue ancestry-correlated binding agent  316  may be associated with an instance  314   a  of the target-related tissue ancestry-correlated binding site  314 .  
      Also, other data may be included in the treatment data  126 . For example, in  FIG. 4 , a treatment agent precursor  402  is shown that refers generally to an agent used to facilitate application of the treatment agent  320 , e.g., an immune-response element that is used to identify/mark/bond with the target-related tissue ancestry-correlated binding site  314  and/or a substance that when metabolized becomes treatment agent  320 , such as with prodrugs.  
      Many other examples of databases and database structures also may be used. Other such examples include hierarchical models (in which data are organized in a tree and/or parent-child node structure), network models (based on set theory, and in which multi-parent structures per child node are supported), or object/relational models (combining the relational model with the object-oriented model).  
      Still other examples include various types of eXtensible Mark-up Language (XML) databases. For example, a database may be included that holds data in some format other than XML, but that is associated with an XML interface for accessing the database using XML. As another example, a database may store XML data directly. Additionally, or alternatively, virtually any semi-structured database may be used, so that context may be provided to/associated with stored data elements (either encoded with the data elements, or encoded externally to the data elements), so that data storage and/or access may be facilitated.  
      Such databases, and/or other memory storage techniques, may be written and/or implemented using various programming or coding languages. For example, object-oriented database management systems may be written in programming languages such as, for example, C++ or Java. Relational and/or object/relational models may make use of database languages, such as, for example, the structured query language (SQL), which may be used, for example, for interactive queries for information and/or for gathering and/or compiling data from the relational database(s).  
      As referenced herein, the treatment system  102  may be used to perform various data querying and/or recall techniques with respect to the treatment data  126 , in order to facilitate treatment and/or diagnosis of the patient  106 . For example, where the treatment data are organized, keyed to, and/or otherwise accessible using one or more of the treatment parameters  302  and/or treatment characteristics  304 , various Boolean, statistical, and/or semi-Boolean searching techniques may be performed.  
      For example, SQL or SQL-like operations over one or more of the treatment parameters  302 /treatment characteristics  304  may be performed, or Boolean operations using the treatment parameters  302 /treatment characteristics  304  may be performed. For example, weighted Boolean operations may be performed in which different weights or priorities are assigned to one or more of the treatment parameters  302 /treatment characteristics  304 , perhaps relative to one another. For example, a number-weighted, exclusive-OR operation may be performed to request specific weightings of desired (or undesired) treatment data to be included (excluded).  
      For example, the clinician  104  may wish to determine examples of the direct end target  306  that are associated with examples of the discriminated end target  308  that are highly discriminated against with respect to delivery of the target-related tissue ancestry-correlated binding agent  316 , for highly-specific delivery of the treatment agent  320 . For example, the clinician  104  may want to know instances of the treatment agent  320  that may be delivered to the lungs as the direct end target  306 , without substantially affecting the pancreas, liver, or other tissue, organ, or organ system/subsystem. In other examples, the clinician may be willing to tolerate lower levels of discrimination (e.g., increased delivery of the treatment agent  320  to other body regions), perhaps because the patient  106  is in an advanced stage of illness. As another example, the clinician  104  may start with a preferred (type of) the treatment agent  320 , and may request from the treatment system  102  various delivery techniques (e.g., target-related tissue ancestry-correlated binding agent  316 ) that may be available, perhaps with varying levels of efficacy.  
      The clinician  104  may specify such factors using, for example, the user interface  132 . For example, the clinician  104  may be able to designate one or more of the treatment parameters  302 /treatment characteristics  304 , and assign a weight or importance thereto, using, for example, a provided ranking system. In this regard, and as referenced herein, it should be understood that the clinician  104  may wish to deliver a particular instance of the treatment agent  320 , e.g., a particular radionuclide to be delivered to a tumor. However, such a treatment agent, if applied by conventional techniques, may be problematic or prohibited (e.g., where a current physiological condition of the patient  106  and/or state of an immune system of the patient  106  is insufficient to allow the clinician  104  to use the desired treatment agent). Moreover, the clinician  104  may not be aware that a suitable target-related tissue ancestry-correlated binding site  314  and/or target-related tissue ancestry-correlated binding agent  316  has (have) been discovered for delivering the treatment agent with a desired/required level of accuracy. However, the clinician  104  may query the treatment system  102  based on the desired treatment agent  320 , and may thereby discover the technique(s) by which the treatment agent may be applied, and with the necessary level of specificity.  
      Similarly, data analysis techniques (e.g., data searching) may be performed using the treatment data  126 , perhaps over a large number of databases. For example, the clinician  104  may perform a physical screening of the patient  106 , and may input some body system, tissue, organ, or organ system/subsystem parameters against which screening is to be performed. Then, the clinician should receive a listing of target-related tissue ancestry-correlated binding sites and/or target-related tissue ancestry-correlated binding agents that are ranked according to some criteria. For example, the clinician  104  may receive a listing of instances of the target-related tissue ancestry-correlated binding site  314  that provide a particularly high or low level of discrimination with respect to a particular direct end target  306 , discriminated end target  308 , and/or treatment agent  320 . In this way, for example, if the patient  106  has an organ or organ subsystem that requires protection from a given instance of the treatment agent  320 , then the clinician  104  may select an instance of the target-related tissue ancestry-correlated binding site  314  and/or of the target-related tissue ancestry-correlated binding agent  316  accordingly, even if some relative sacrifice of binding strength/accuracy is associated with such a selection.  
      By way of further example, other parameters/characteristics may be factored in. For example, elimination pathways may be tracked, databased, and/or weighted for use in the treatment data  126  and/or the treatment system  102 . For example, if a particular instance of the target-related tissue ancestry-correlated binding agent is especially readily eliminated by the liver, then, in a case where the patient  106  has impaired hepatic function, such an instance may be selected for delivering the treatment agent  320 , even if an otherwise superior instance of the target-related tissue ancestry-correlated binding agent  316  is known. Algorithms implementing such query/recall/access/searching techniques may thus use Boolean or other techniques to output, for example, a thresholded, rank-ordered list. The treatment logic  128  may then assign a key or other identifier to such a list(s), for easier use thereof the next time a like query is performed.  
      Design and testing of querying techniques in particular implementations of the treatment system  102  may involve, for example, entry of candidate treatment parameters  302 /treatment characteristics  304  (or instances thereof) into a database(s), along with associated test results and/or affinity metrics that may be used to determine/weight targets or sets of targets. Then, an identifier may be generated that is unique to the target(s) set(s).  
      Still other examples/applications include avoiding an auto-immune response of the patient  106 , in order to achieve a desired result. For example, the treatment system  102  may be used to determine/catalog/use treatment data that relates to treatment parameters  302 /treatment characteristics  304  that are known or suspected to avoid self-epitopes (e.g., those unlikely to generate an undesired autoimmune response).  FIG. 5  illustrates another alternative embodiment of treatment data associated with the clinical system  100  of  FIG. 1 , with specific examples of treatment data. In particular, all of  FIGS. 5-7  provide or refer to example results from related technical papers, which are specifically referenced below.  
      For example, rows of the table of  FIG. 5  (e.g., rows  502 ,  504 , and  506 , respectively) refer to examples that may be found in Oh, P. et al., “Subtractive Proteomic Mapping of the Endothelial Surface in Lung and Solid Tumours for Tissue-Specific Therapy,” Nature, vol. 429, pp. 629-635 (Jun. 10, 2004), which is hereby incorporated by reference in its entirety, and which may be referred to herein as the Oh reference.  
      In the Oh reference, it is generally disclosed that regions of endothelium may change or alter over time, based on what tissues are in the vicinity thereof, as referenced herein. The Oh reference, for example, identified lung-induced and/or lung-specific endothelial cell surface proteins based on a hypothesis that a surrounding tissue (micro) environment of the endothelial cell surface proteins modulates protein expression in the vascular endothelium. The Oh reference identified specific proteins that were found to be expressed at an endothelial surface by specifying two regions of interest (e.g., a “lung region” and a “non-lung region”), and then determining proteins within the two regions. Then, by subtracting the two sets of proteins from one another, non-common proteins were identified.  
      In this way, uniquely occurring proteins at a specific endothelial site (e.g., the target-related tissue ancestry-correlated binding site  314  at a specific direct intermediate target  310 ) were identified. Then, these uniquely-occurring proteins were used as targets for generated antibodies. As a result, it was possible to target, for example, lung-specific tissues as opposed to non-lung-specific tissues, and/or to target tumors as opposed to non-tumor tissues. More specifically, for example, it was determined to be possible to target tumor-induced endothelial cell proteins (e.g., target-related tissue ancestry-correlated binding sites  314 ) for delivery thereto of drugs, imaging agents, and/or radiation agents (e.g., treatment agents  320 ) that were attached to appropriate antibodies (target-related tissue ancestry-correlated binding agents  316 ).  
      Thus, to set forth specific examples, a row  502  illustrates an example in which the direct end target  306  includes a treatment parameter of “lung tissue.” In this example, the discriminated end target  308  includes “non-lung tissue.” The direct intermediate target  310  includes endothelial tissues that are proximate to the lung tissue, while the discriminated intermediate target  312  includes endothelial tissue that is proximate to the non-lung tissue.  
      The target-related tissue ancestry-correlated binding site  314  in this example includes aminopeptidase-P (APP), which is a protein that was detected substantially only in endothelial plasma membranes from the lung tissue (e.g., direct end target  306 ). In order to take advantage of the immuno-accessibility of APP in vivo, I 125 -labeled monoclonal antibodies were used as the target-related tissue ancestry-correlated binding agent  316 , and were intravenously injected into test rats. Subsequent imaging of the lungs illustrated rapid and specific targeting of APP antibody to the lung (e.g., direct end target  306 ), with significantly reduced accumulation of the injected dose at non-lung tissue (e.g., the discriminated end target  308 ). Thus, by selecting the treatment agent  320  to include radio-immunotherapy via low levels of radionuclides (e.g., 100 μCi of I 125 ), a treatment agent delivery mechanism relative to target-related tissue ancestry-correlated binding agent  318  may involve essentially direct delivery, in that the radionuclide(s) may be affixed to the monoclonal APP antibodies, similarly to how the I 125  was affixed as described in Oh, et al. Further, although the term antibody is used herein in various examples, it should be understood that other immuno-reactive features of the adaptive immune system also may be used in a similar or analogous manner, including entities that serve to mediate antibody generation, such as, for example, helper T cells or dendritic cells.  
      In the row  504  of  FIG. 5 , a conceptual secondary example drawn from/based on the Oh reference is included, in order to illustrate various concepts described herein, e.g., with respect to  FIGS. 1-4 . Specifically, in the row  504 , various ones of the treatment parameters and/or treatment characteristics are the same as in the row  502 , except that a second example of the target-related tissue ancestry-correlated binding agent  316  is illustrated generically as “Binding Agent X,” and, similarly, a second example of a generically-referenced treatment agent  320  is illustrated as “Treatment Agent X.” As such, the row  504  illustrates, for example, that two separate instances of the target-related tissue ancestry-correlated binding agent  316  and/or the treatment agent  320  may be associated with, e.g., an instance of either the direct end target  306 , and/or with an instance of the target-related tissue ancestry-correlated binding site  314 .  
      The row  506  illustrates another example from the Oh reference. In the row  506 , the direct end target  306  is illustrated as “diseased lung tissue,” while the discriminated end target  308  is illustrated as “non-diseased lung tissue.” Thus, the direct intermediate target  310  is illustrated as “endothelial tissue proximate to the diseased lung tissue,” while the discriminated intermediate target  312  is illustrated as “endothelial tissue that is proximate to non-diseased lung tissue.” 
      Then, the target-related tissue ancestry-correlated binding site  314  is illustrated as fifteen differentially-expressed proteins (e.g., expressed according to the subtractive techniques described herein) associated with the direct intermediate target  310 , e.g., the endothelial tissue proximate to the diseased lung tissue. As a result, the target-related tissue ancestry-correlated binding agent  316  is selected and illustrated as I-labeled monoclonal APP antibodies that may be generated for one or more of the fifteen differentially-expressed proteins. As in the row  502 , the treatment agent delivery mechanism relative to target-related tissue ancestry-correlated binding agent  318  may involve essentially direct attachment of the treatment agent  320  that is illustrated as radio-immunotherapy via low-levels of radionuclides. In this way, such radionuclides may be concentrated in, and may thereby destroy, tumors. In particular, for example, an identified tumor target was the 34 KDa protein recognized by annexin A1 (AnnA1) antibodies, which was significantly present in substantially only in tumor endothelial plasma membrane.  
       FIG. 6  illustrates additional alternative embodiments of treatment data associated with the clinical system  100  of  FIG. 1 , with specific examples of treatment data. In  FIG. 6 , a row  602  illustrates examples that may be found in Essler et al., “Molecular Specialization of Breast Vasculature: A Breast-Homing Phage-Displayed Peptide Binds to Aminopeptidase P in Breast Vasculature,” Proceedings of the National Academy of Sciences, vol. 99, No. 4, pp. 2252-2257 (Feb. 19, 2002), which is hereby incorporated by reference in its entirety, and which may be referred to herein as the Essler reference.  
      In the Essler reference, a plurality of peptides (e.g., two or more amino acids joined together via a peptide bond) having a general structure of CX7C (where C is cysteine and X is any amino acid) I-labeled monoclonal antibodies were injected into mice. Then tissues of interest were observed to determine a presence of phage(s), and thereby to determine which peptide of the plurality of peptides honed in on the observed tissue(s). In this way, it was determined that the CPGPEGAGC peptide was useful in providing a homing point for phages of the patient&#39;s immune system, and, in particular, was useful as a binding agent for the breast tissue, while not binding to pancreas tissue. Although these specific examples of peptides are provided for illustration and explanation, it should be understood that the term peptide as used herein may refer to virtually any lineal peptide-bonded string of amino acid residues, which include various structures thereof, unless context dictates otherwise. For example, a lipopeptide may be interpreted to include virtually all lipoproteins, while glycopeptides may include virtually all glycoproteins.  
      Thus, in the row  602 , the direct end target  306  is illustrated as breast tissue, while the discriminated end target  308  is illustrated as pancreas tissue. The direct intermediate target  310  is illustrated as vascular beds of breast tissue, while the discriminated intermediate target  312  is illustrated as vascular beds of pancreas tissue.  
      The target-related tissue ancestry-correlated binding site  314  includes a protein, aminopeptidase-P (APP), of the vascular bed of breast tissue. The target-related tissue ancestry-correlated binding agent  316  includes a cyclic nonapeptide known as the CPGPEGAGC peptide, which is shown in the Essler paper to home to the aminopeptidase P receptor. The treatment agent precursor  402  is shown to include phages, which were essentially directly delivered via the CPGPEGAGC peptide to the APP of the vascular bed of breast tissue, and which facilitate attachment of additional/alternative treatment agents  320  to the APP.  
      A row  604  of  FIG. 6  illustrates an example from Hood et al., “Tumor Regression by Targeted Gene Delivery to the Neovasculature,” Science, vol. 296, pp. 2404-2407 (Jun. 28, 2002), which is incorporated by reference in its entirety and which is referred to herein as the Hood reference. The Hood reference refers to the molecule integrin avB 3  that plays a role in endothelial cell survival during formation of new blood vessels in a given region, and is preferentially expressed therein. A cationic polymerized lipid-based nanoparticle was synthesized and covalently coupled to a small organic avB 3  ligand; that is, the ligand was demonstrated to serve as a binding agent for the integrin avB 3  that is preferentially expressed in endothelial cells.  
      Accordingly, in the row  604 , melanoma tumors were used as the direct end target  306 , while the discriminated end target  308  is shown as surrounding non-tumor tissues. The direct intermediate target  310  is illustrated as endothelial cells having integrin avB 3 , while the discriminated intermediate target  312  is shown as endothelial cells without integrin avB 3 . Thus, the target-related tissue ancestry-correlated binding site  314  is shown to include the integrin avB 3 , while the target-related tissue ancestry-correlated binding agent  316  is shown to include the avB 3  ligand that attaches to the integrin avB 3 . The treatment agent  320  included a gene selected to disrupt formation of new blood vessels in the tumor(s), which was delivered using the cationic polymerized lipid-based nanoparticle(s), and which thereby deprived the tumor(s) of blood and destroyed the tumor(s).  
       FIG. 7  illustrates additional embodiments of treatment data associated with the clinical system  100  of  FIG. 1 , with specific examples of treatment data. In a row  702 , an example is illustrated from McIntosh et al., “Targeting Endothelium and Its Dynamic Caveolae for Tissue-Specific Transcytosis in vivo: A Pathway to Overcome Cell Barriers to Drug and Gene Delivery,” Proceedings of the National Academy of Sciences, vol. 99, no. 4, pp. 1996-2001 (Feb. 19, 2002), which is hereby incorporated by reference and which may be referred to herein as the McIntosh reference. In the McIntosh reference, endothelial cell plasma membranes from the lungs were analyzed to determine monoclonal antibodies targeted thereto. Additionally, the McIntosh reference illustrated use of the caveolae  124  to allow the treatment agent  320  to cross the endothelium and be delivered directly to lung tissue.  
      Thus, in the row  702 , the direct end target  306  is shown as lung tissue, while the discriminated end target  308  is shown as non-lung tissue. The direct intermediate target  310  is shown as endothelial cell caveolae proximate to the lung tissue, while the discriminated intermediate target  312  is shown as endothelial cell caveolae that is distal from the lung tissue.  
      The target-related tissue ancestry-correlated binding site  314  is shown as a determined/selected antigen to which the monoclonal antibody TX3.833 binds, so that the target-related tissue ancestry-correlated binding agent  316  is shown as the monoclonal antibody TX3.833 itself. In this way, the treatment agent  320  of gold affixed directly to the TX3.833 antibody was transported over the endothelial plasma membrane into the tissues of interest (e.g., lung tissues); in other words, the caveolae  124  was used to conduct transcytosis.  
      A row  704  illustrates an example from Zhiwei et al., “Targeting Tissue Factor on Tumor Vascular Endothelial Cells and Tumor Cells for Immunotherapy in Mouse Models of Prostatic Cancer,” Proceedings of the National Academy of Sciences, vol. 98, no. 21, pp. 12180-12185 (Oct. 9, 2001), which is hereby incorporated by reference in its entirety, and which may be referred to as the Zhiwei reference. In the Zhiwei reference, a “tissue factor” is identified as a transmembrane receptor that forms a strong and specific complex with an associated ligand, factor VII (fVII). Such tissue factor, although not normally expressed on endothelial cells, may be expressed on tumor endothelial cells of the tumor vasculature.  
      Thus, in the example of the row  704 , the direct end target  306  includes prostrate tumors, while the discriminated end target  308  includes all other tissues. The direct intermediate target  310  includes tissue factor(s) expressed by/on endothelial cells near the tumor(s) and by/on the tumor itself. The target-related tissue ancestry-correlated binding site  314  includes the tissue factor, while the target-related tissue ancestry-correlated binding site agent  316  includes the factor VII (fVII), the ligand for the tissue factor. In this way, the direct treatment agent  320  of a Fc effector domain was used to provide a marker for an induced immune response.  
      In a row  706 , an example is illustrated from Kaplan et al., “VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche,” Nature, vol. 438, no. 4, pp. 820-827 (December 2005), which is hereby incorporated by reference and which may be referred to herein as the Kaplan reference. In the Kaplan reference, metastasis is described as a process in which tumor cells mobilize bone-marrow cells to form a site or “pre-metastatic niche” at particular regions (distant from the primary tumor itself), at which the subsequent metastasis may then develop. More specifically, Kaplan describes the idea that cells of a tumor may secrete a molecular/humoral factor(s) that mobilizes bone marrow cells and stimulates fibroblast cells at a distant (future metastatic) site, thereby upregulating fibronectin (a binding, tissue-promoting protein) that serves as a “docking site” for the bone marrow cells. Some of the bone marrow cells were positive for proteins characteristic of haematopoietic progenitor cells, including, for example, vascular endothelial growth factor receptor  1  (VEGFR1), which, in turn, is described as promoting attachment and motility of tumor cells, thereby leading to metastasis. For example, protease production associated with the bone marrow cells may lead to growth factors (e.g., vascular endothelial growth factor (VEGF) that support the developing niche, through, e.g., angiogenesis). In other words, the VEGFR1-positive bone marrow cells serve to form the “pre-metastatic niche” by colonizing a site distant from the tumor, so that subsequently-arriving tumor cells find a hospitable environment at such a site.  
      Thus, in the example of the row  706 , the direct end target  306  may include one-or-more metastatic and/or pre-metastatic niches or sites that are distant from a primary tumor. For example, such niches may be present in the lungs when the primary tumor includes a melanoma. Then, the discriminated end target  308  may include tissues other than these metastatic niches. The direct intermediate target  310  may include endothelial cells at the metastatic niches, while the discriminated intermediate target  312  may include endothelial cells at other locations. Additionally and/or alternatively, the direct intermediate target  310  may include endothelial cellular structures at the metastatic or pre-metastatic niches, while the discriminated intermediate target  312  may include endothelial cellular structures at other locations. In the example of the row  706 , the target-related tissue ancestry correlated binding site  314  includes VEGFR1, which, as referenced above, includes a receptor protein on the endothelial cells (to which VEGF may bind). In this case, and as referenced in the Kaplan reference, the target-related tissue ancestry correlated binding agent  316  may include an antibody to VEGFR1, so that the treatment agent delivery mechanism relative to the target-related tissue ancestry correlated binding agent  318  includes an essentially direct delivery of this antibody, where the antibody to VEGFR1 thereby serves as the treatment agent  320  by blocking the VEGFR1 and preventing formation of, occupying, and/or blocking subsequent interactions with development of the pre-metastatic niche. Of course, the row  706  includes merely one example of target-related tissue ancestry correlated binding site(s) and/or target-related tissue ancestry correlated binding agent(s) that may be located within, or in association with, the pre-metastatic niche(s), where appropriate discovery and/or targeting thereof may be performed by any of the techniques described herein, or other techniques. Moreover, it should be understood from the above description that such target-related tissue ancestry correlated binding site(s) and/or target-related tissue ancestry correlated binding agent(s) may be time-dependent, e.g., with respect to formation and metastasis of the primary tumor. Accordingly, application of the just-referenced techniques may be determined and/or occur based on such time-dependencies, e.g., by applying the techniques for patients at high risk of metastatic disease, but for whom metastatic disease has not yet actualized in the form of established metastases.  
      In other, related, examples, the treatment(s) just described (e.g., use of an antibody to VEGFR1) should be understood to represent merely an example(s) of how to reduce or eliminate development of the pre-metastatic niche(s) and/or metastasis of the primary tumor. For example, molecular addressing as described herein may be used to slow or stop the upregulation of fibronectin. In such examples, and considering the time-dependent nature of metastasis and treatment just referenced, the alternative treatment modalities (e.g., regulating a presence or development of VEGFR1 and fibronectin) may be seen as complementary to one another. For example, such treatment modalities may be implemented cyclically for the patient  106 , the better to disrupt the pre-metastatic/metastatic pathway as a whole, and thereby to increase an efficacy of the overall treatment of the patient  106 . Of course, similar comments apply to treatment modalities applied at other points in the pathway, as well as to other pathways, as would be apparent.  
       FIG. 8  illustrates an example screenshot of a graphical user interface for filtering predictive data. In  FIG. 8 , an example of the user interface  132  of  FIG. 1  is illustrated as providing a graphical illustration  802  of the patient  106 . For example, the graphical illustration  802  may include an image of some or all of the patient  106 , where the image may include various colors, highlights, or other visual indicators designed to provide information regarding the patient  106 , or regarding a diagnosis or treatment of the patient  106 . The graphical illustration  802  may illustrate internal organs of interest, and surrounding or related body portions, with varying (and variable) levels of resolution. For example, user controls (not shown in  FIG. 8 ) may be provided that allow the clinician  104  to view the graphical illustration  802  by zooming in or out, or by moving a viewing focus of/on the graphical illustration  802 . Although illustrated in  FIG. 8  as an outline, the graphical illustration  802  may include other visual representations of the patient  106 , which may be generic to a class of patient or specific to a particular patient, and which may include a photograph or other illustration derived from image sensor(s), or a three-dimensional representation of the patient  106 . Additionally, or alternatively, the graphical illustration  802  may include a chart, graph, diagram, table, or other representation of data that may be useful to the clinician  104  in diagnosing or treating the patient  106 .  
      In the example of  FIG. 8 , the user interface  132  includes a plurality of fields  804 ,  806 ,  808 ,  810 ,  812 , and  814 . In some implementations, the fields  804 - 814  allow the clinician to access, analyze, or otherwise consider or use the treatment data  126  of  FIG. 1  to diagnose and/or treat the patient  106 . For example, as referenced herein, the clinician  104  may determine or consider treatment techniques to select and deliver an appropriate type and/or level of a treatment agent, with an appropriate degree of accuracy, to a desired (direct) end target, while minimizing a negative impact of such a selection/delivery, if any, on other regions of the body of the patient  106 . In some implementations, the user interface  132  thus provides the clinician  104  with bases for speculation or conjecture regarding a potential course of treatment or research that may be undertaken with regard to the patient  106 . In other words, for example, the user interface  132  allows the clinician  104  to hypothesize about an efficacy, risk, unwanted impact, or side effect of a particular course of treatment that may be undertaken.  
      For example, the field  804  may include a drop-down menu by which the clinician  104  may select a direct end target that is desired for treatment or analysis. In the example of  FIG. 8 , the field  804  is illustrated as showing a selection of “cancer cells in lung” as the direct end target. Meanwhile, the field  806  illustrates a selection of “radionuclides” as a potential treatment agent.  
      As described herein, delivery of radionuclides or other appropriate treatment agents to a desired bodily location may be accomplished by using a “molecular address” provided by a target-related tissue ancestry-correlated binding site, e.g., by associating the treatment agent (radionuclides) with a target-related tissue ancestry-correlated binding agent that is known to deliver the treatment agent to the target-related tissue ancestry-correlated binding site (and thereby, for example, to surrounding target tissue), while discriminating against, or avoiding, ancillary or undesired delivery of the treatment agent to non-target tissue(s). Thus, in the example of  FIG. 8 , once the clinician  104  selects a desired direct end target using the field  804 , and a desired treatment agent in the field  806 , then the clinician  104  may select “request suggestion” in the field  808  associated with a target-related tissue ancestry-correlated binding agent, as shown. In this case, the system  100  or similar system (e.g., the system  900  of  FIG. 9 , discussed in more detail, below) may thus provide a suggestion for the target-related tissue ancestry-correlated binding agent of “I labeled monoclonal antibodies” in the field  810 , for consideration and possible use by the clinician  104  in applying the treatment agent (radionuclides) of the field  806  of the direct end target (cancer cells in lung) of the field  804 .  
      Of course,  FIG. 8  and the above discussion provide merely a few examples of how the user interface  132  may be used in conjunction with the treatment logic  128  of the treatment system  102  to access the treatment data  126 . In other examples, the clinician  104  may request a suggestion for the direct end target in the field  804 , or may request a suggestion for the treatment agent  806 , or, on the other hand, may simply specify all desired treatment parameters (in which case no suggested treatment parameter need be provided in the field  810 ). Further, although  FIG. 8  is illustrated for the sake of example as including fields for the direct end target, the treatment agent, and the target-related tissue ancestry-correlated binding agent, it should be understood that any of the various treatment parameters mentioned herein, or other treatment parameters, may be selected or provided in conjunction with the user interface  132 .  
      However the treatment parameter(s) are selected and/or provided in the user interface  132 , the graphical illustration  802  may be used to provide possible outcomes of a use of the treatment parameter(s) with respect to one or more body portions. For example, in the illustrated example of  FIG. 8 , where the treatment parameters of the fields  804 - 810  are selected or provided, the graphical illustration  802  may be used to illustrate a possible outcome of the use of the treatment parameters with respect to the lungs  108  and/or the pancreas  110 . For example, since cancer cells in the lungs  108  are intended to be used as the direct end target, as specified in the field  804 , the graphical illustration  802  may be used to illustrate an effect of delivering the specified treatment agent (radionuclides) of the field  806  to the lungs  108 , using the target-related tissue ancestry-correlated binding agent suggested in the field  810  (also using, it will be appreciated, the appropriate target-related tissue ancestry-correlated binding site associated with the lungs  108  to which the target-related tissue ancestry-correlated binding agent is known to bind). For example, a color scheme or other visual indicator(s) may be used to indicate an efficacy of the specified treatment parameters with respect to the lungs  108 , e.g., by providing the illustration of the lungs  108  in different colors to indicate the efficacy of the specified treatment parameters. Of course, other audio or visual indicators may be used, e.g., the graphical illustration  802  may include a brightness or other visual aspect of the illustration of the lungs  108  that is varied in direct or indirect correspondence with an efficacy of the specified treatment parameters.  
      As a result, the clinician  104  may, for example, observe and judge an efficacy of a plurality of successively-specified treatment parameters, simply by selecting or requesting examples and combinations thereof, using the fields  804 - 810 . By use in part of such visual indicators as those just described, the clinician  104  may quickly and easily make judgments about which treatment parameter(s) may be most useful in a given diagnostic, treatment, or research scenario.  
      In some implementations, the graphical illustration  802  may be used to provide other possible outcomes of the use of the treatment parameter(s), beyond illustrating an efficacy thereof. For example, the graphical illustration  802  may automatically illustrate side effects, unwanted impacts, or other risks, ambiguities or consequences of using the specified treatment parameter(s). For example, as described herein, it may be the case that use of the specified treatment parameter(s) may result in an undesired side effect of, for example, delivery of the treatment agent (e.g., radionuclides) to other body portions. Accordingly, the graphical illustration  802  may illustrate body portions that may be affected by the use of the treatment parameter(s) in an undesired, unwanted, and/or detrimental manner. For example, the graphical illustration  802  may include a representation of the pancreas  110 , which may be affected by the treatment agent (radionuclides) in an undesired manner. Again, visual indicators may be used to indicate a nature and/or extent of the undesired effect, using, e.g., a designated color scheme, highlighting, numerical or graphical representation, or other visual indications.  
      Thus, again, the clinician  104  may gain useful information for diagnosing or treating the patient  106 , or for general research/inquiry into uses of different treatment parameters. For example, by specifying different (combinations of) treatment parameters, the clinician may observe an efficacy of a desired treatment, relative to a nature and extent of unwanted impacts thereof. For example, the clinician  104  may be reminded (or made aware) of certain side effects that may not otherwise have been considered or known, and may respond accordingly. For example, if the patient  106  is known to have a weakened or somewhat dysfunctional pancreas, then different treatment parameters may be selected to find combinations thereof that retain a desired level of efficacy, while avoiding dangerous or unwanted application of the treatment agent to the pancreas  110 .  
      In providing the graphical illustration  802 , including possible outcomes (both beneficial and detrimental) of the use of the specified treatment parameters, the user interface  132  may access and use the treatment data  126 , using the treatment logic  128 . In the example of  FIG. 8 , the treatment data  126  may include a plurality of datasets used by the treatment logic  128  to provide the graphical illustration  802 , where each dataset may be associated with at least one predictive basis for providing the possible outcome(s) of the use of the various treatment parameters.  
      For example, a first such dataset may be associated with a first predictive basis that may include previous studies or trials performed on human subjects. That is, results of previous studies or trials performed on human subjects may be stored in the first dataset, and these results may be tagged, identified, or otherwise characterized within the treatment data  126  as having a certain type or degree of predictive value. For example, the first dataset may be characterized as being more predictively useful than results from a second dataset associated with studies or trials based on animals, simply by virtue of having been performed on human subjects. In other examples, the results in the first dataset may be characterized as having been performed in a certain timeframe or environment, under certain funding and/or procedural guidelines, within a defined area or type of medical practice, or having some other predictive basis and/or value. In these and other such examples, the first dataset may be designated to have more or less predictive value than a second dataset that also stores results of studies or trials performed on human subjects, but where the identified characteristic(s) is different in quantity or quality (e.g., performed in a different timeframe or environment, or under more or less stringent funding and/or procedural guidelines, or in a different area of medical practice (e.g., holistic/alternative as compared to traditional)).  
      In the example of  FIG. 8 , a field  812  is included that allows the clinician  104  to specify one or more datasets to be used by the treatment logic  128  in generating the graphical illustration  802 . For example, the field  812  illustrates that the clinician  104  may select one or more datasets associated with human studies, animal studies, computer simulations, “in silico” datasets, speculated datasets, or aggregated datasets (where, for example, the clinician  104  may specify different combinations or aggregations of the different datasets, e.g., by selecting multiple ones of the listed examples). Of course, these are just examples, and any other knowledge source may be used, as would be apparent, including, for example, any type of in vivo or in vitro or in silico study.  
      In this way, for example, the clinician  104  may use the user interface  132  as a convenient tool to perform analysis, speculation, or prediction of a possible outcome of the use of specified treatment parameters, based on the different datasets having different predictive bases. For example, for the treatment parameters specified in the fields  804 - 810 , the clinician may first select “human studies” in the field  812 , whereupon the user interface  132  may provide the graphical illustration  802  with a first illustration of the lungs  108 , perhaps in association with a certain color or other visual indicator designed to illustrate an efficacy of the treatment parameters with respect to the lungs  108  (or, more specifically, with respect to certain types of cancer cells within the lungs  108 ). In this first example, the pancreas  110  may not initially be illustrated (or may be illustrated but not visually marked or altered), since, for example, the human studies providing the first predictive basis of the first dataset may not have shown any adverse effects with respect to the pancreas  110 .  
      Then, the clinician may specify a second dataset having a second predictive basis, such as, for example, a dataset associated with “animal studies,” as selected from the field  812 . In this case, the user interface  132  may modify the graphical illustration  802  to provide a modified graphical illustration that includes the pancreas  110  (and/or a visual indicator associated therewith), and that thereby illustrates that the results of the second dataset indicate that a possible outcome of the use of the specified treatment parameters includes unwanted application of the treatment agent to the pancreas  110 .  
      As a result, for example, the clinician  104  may make a more informed decision about a future course of action regarding a diagnosis or treatment of the patient  106 . For example, the fact that the animal studies of the second dataset indicate the possible outcome of unwanted impact on the pancreas  110  may not be considered to be conclusive with regard to predicting the same or similar effect on the patient  106  (assuming that the patient  106  is human in this example). Nonetheless, for example, the clinician  104  may be reminded of a possible side effect or other concern that may otherwise have been discounted or forgotten, or, as another example, where the clinician  104  knows that the patient  106  has a weakened or dysfunctional pancreas, the above-described information provided by the user interface  132  may be sufficient for the clinician  104  to continue specifying different treatment parameters in the fields  804 - 810 , in an attempt to determine a more appropriate treatment for the patient  106 .  
      Similar comments apply regarding an efficacy of specified treatment parameter(s) with regard to the lungs  108 . For example, the first dataset associated with the human studies may indicate a certain degree of efficacy of the specified treatment parameters of the fields  804 - 810  (e.g., by way of an appropriate visual indicator, such as color), while the second dataset associated with the animal studies may indicate a greater (or lesser) degree of efficacy. In this case, the clinician  104  may select the specified treatment parameters for use with the patient  106 , as compared to alternate treatment parameters. That is, where the clinician  104  is choosing between two or more possible courses of treatment, the clinician  104  may arrive at a selection of a treatment based on a consideration of possible outcomes illustrated by the user interface  132 , based on different ones of the datasets of the field  812 .  
      In addition to diagnosis and treatment of the patient  106 , the user interface  132  may be used, for example, as a research or speculation tool for determining and assessing possible future treatments. For example, the clinician  104  may be in the process of determining a future course of research with respect to different (combinations of) treatment parameters. In deciding between the different courses of research that may be taken, the clinician  104  may consider possible outcomes of the treatment parameters, using the various datasets of the field  812 . For example, if a particular combination of treatment parameters shows a high degree of efficacy (and/or a low degree of unwanted side effects) based on multiple ones of the datasets of the field  812 , then the clinician  104  may consider that the particular combination merits further research or clinical-use consideration.  
      Further in  FIG. 8 , a field  814  allows the clinician  104  to apply a filter criteria to the dataset(s) specified in the field  812 . For example, the filter criteria may remove portions of the current dataset(s) that the clinician  104  may feel have less predictive value in determining the possible outcome(s) of using the specified treatment parameters. For example, the clinician may begin consideration of possible outcomes of the specified treatment parameters by selecting “aggregation” in the field  812 , so that the graphical illustration  802  illustrates the possible outcome of use of the treatment parameters based on all of the datasets of the field  812 . Then, the clinician  104  may selectively remove a contribution of a selected one or more of the datasets, by, for example, selecting a dataset associated with “animal studies” in the field  814 , or selecting a filter criteria of “computer simulation(s)” to remove computer-simulated results from the combined datasets.  
      In other examples, the filter criteria may not correspond directly or in a one-to-one relationship with one of the datasets of the field  812 . For example, the filter criteria may filter information from a combination of datasets, e.g., information that is common to each of the datasets. For example, if the clinician  104  selects “human studies” and “animal studies” using the field  812 , then the clinician  104  may select “after 1990” in the field  814  to remove all results from both datasets that were collected prior to 1990. Similarly, as shown in  FIG. 8 , the field  814  may be used to filter results from one or more datasets based on whether the results were obtained in a particular geographical region (e.g., “region x”), or were obtained in studies conducted according to a particular protocol (e.g., “protocol x”).  
      Thus,  FIG. 8  illustrates an example of a graphical user interface including at least a first portion (e.g., one or more of the fields  804 - 814 ) configured to receive a first request to provide a graphical illustration (e.g., the graphical illustration  802 ) of a first possible outcome of a use of a treatment parameter with respect to at least one body portion (e.g., the lungs  108  and/or the pancreas  110 ), based on a first dataset associated with a first predictive basis (e.g., the first dataset/first predictive basis selected using the field  812 ).  FIG. 8  further illustrates that such a graphical user interface may include at least a second portion (e.g., one or more of the fields  804 - 814 ) configured to receive a second request to provide a modified graphical illustration (e.g., a modified version of the graphical illustration  802 ) of a second possible outcome of the use of the treatment parameter, based on a second dataset associated with a second predictive basis (e.g., the second dataset/second predictive basis selected using the field  812 ). Thus, the graphical user interface also may include a third portion configured to illustrate the graphical illustration and the modified graphical illustration (e.g., the portion of the user interface  132  of  FIG. 8  including the graphical illustration  802 ), so as, for example, to include at least a portion of the at least one body portion (e.g., at least a portion of the lungs  108 ), and/or to include one other body portion (e.g., the pancreas  110 ) in addition to the at least one body portion (e.g., the lungs  108 ).  
       FIG. 9  illustrates an alternative embodiment of the clinical system of FIG. I in which the clinical system is configured to provide access to predictive data. Thus,  FIG. 9  illustrates examples by which the user interface  132  may be used to access or otherwise interact with the treatment data  126 , in order to provide, for example, the various features, functionalities, and effects described above with respect to  FIG. 8 .  
      In the example of  FIG. 9 , the user interface  132  is illustrated as containing generic elements  902  and  904 , e.g., a submission element  902  and a display element  904 . Generally, the submission element  902  may include any icon, button, field, menu, or box that may be used by the clinician  104  to select, submit, or request information. The display element  904  may include any element of the user interface  132  used to provide information to the clinician  104 , where it should be understood that in some cases the submission element  902  and the display element  904  may include the same element, or related elements, since the clinician  104  may enter or select data using a given element and then may view the results of the entry or selection using the same element. Thus, and as should be apparent from  FIG. 8 , the submission element  902  may include, for example, any of the fields  804 ,  806 ,  808 ,  812 , or  814 , since the clinician  104  may submit treatment parameters, datasets, and/or filter criteria therewith. Meanwhile, any of the fields  804 - 814  may be considered to be an example of the display element  904 , since any of these may be used to display information (e.g., a result of a selection of a treatment parameter, dataset, or filter criteria). Of course, the graphical illustration  802  is another example of the display element  904 .  
      Thus, for example and as described herein, the clinician  104  may utilize the submission element(s)  902  to select the treatment parameters (or to request a suggestion of one or more treatment parameters), or to specify one or more datasets to be used in providing the possible outcome(s) of using the treatment parameters, or to specify a filter criteria to be used in filtering the dataset(s). For example, when the clinician  104  uses the field  812  to select the dataset “human studies,” then this submission is passed to the treatment logic  128 , or, more specifically, is passed to an event handler  906  that receives the submission and performs an initial classification, logging, routing, or other handling of the type and value of the submission event, e.g., here, the type including a specification of a dataset to be used and the value including the selected dataset “human studies.” 
      For example, a submission event associated with a use of the submission element  902  by the clinician  104  may be passed by the event handler  906  either to dataset view logic  908  and/or filter logic  910 . As described in more detail herein, the dataset view logic  908  and the filter logic  910  represent aspects of the treatment logic  128  associated with analyzing specified treatment parameters with respect to specific portions (e.g., datasets) of the treatment data  126 , so as, for example, to provide the uses and effects described above with respect to the graphical illustration  802 , e.g., by using display update logic  912  to update the display element(s)  904 .  
      More specifically, for example, the dataset view logic  908  may be used to analyze a submission event from the event handler  906  and determine, for example, that the clinician  104  has selected both “human studies” and “animal studies” using the field  812 . The dataset view logic  908  may then interact with a query generator  914  of the DBMS engine  130  to generate a query that may be passed by a database interface  916  to the treatment data  126 . In this case, it also may occur that the event handler  906  may pass a second submission event (which may occur concurrently or in a sequence), in which the clinician  104  selects “before 1990” as a filter criteria in the field  814 , to the filter logic  910 . Thus, the event handler  906  is responsible for correlating the two submission events, so that the filter logic  910  may correspond the specified filter criteria against the (in this case, two) datasets specified to the dataset view logic  908 .  
      In these and other examples, then, the treatment logic  128  may interact with the DBMS engine  130  to construct a query and pass the query to the treatment data  126 . For instance, in the example just given, a query may be built that includes a Boolean combination of a first dataset associated with “human studies” AND a second dataset associated with “animal studies,” where the query is generated with a form and structure that is appropriate for the treatment data  126  (e.g., using the Structured Query Language (SQL) in a case where the treatment data  126  implements a relational database).  
      In  FIG. 9 , example data results and/or datasets are referenced to  FIG. 5 , where, as shown in  FIG. 5 , rows  502  and  504  include (abbreviated) data results for a direct end target  306 , a target-related, tissue ancestry-correlated binding agent  316 , and a treatment agent  320 . In this case, for example, data from the row  502  may be associated with a tag  918  indicating that data from the row  502  is associated with human studies and should therefore be included in a first dataset, while data from the row  504  may be associated with a tag  920  indicating that data from the row  504  is associated with animal studies and should therefore be included in a second dataset (where such examples are intended to illustrate a use of the tags  918 ,  920  with respect to a query from the DBMS engine  130 , and are not intended, necessarily, with specific reference to the Oh reference of  FIG. 5 ). In some implementations, for example, the tags  918  and  920  may be associated with use of the extensible Markup Language (XML) in constructing the treatment data  126 , where use of XML or other semi-structured databases is discussed in more detail, herein. In this case, then, the database interface  916  may include an XML interface.  
      It should be understood, then, that the tags  918 ,  920  may be used in generating and executing queries against the treatment data  126  by either the dataset view logic  908  or the filter logic  910 . For example, the filter logic  910  may interact with the query generator  914  to generate a query against the treatment data  126  (or against a result set of a query generated in conjunction with the dataset view logic  908 ), using the tags  918 ,  920  to identify, and thereby remove/exclude, data that matches the filter criteria from a corresponding result set.  
      Once an appropriate result set(s) has been generated by the dataset view logic  908  and/or the filter logic  910 , the display update logic  912  may be used to update the display element  904  appropriately, as referenced herein. For example, the display update logic  912  may include logic for implementing the color schemes mentioned above, or for providing any other visual indicator(s) that may be used to convey information in association with the graphical illustration  802 .  
       FIG. 10  illustrates an operational flow representing example operations related to filtering predictive data. In  FIG. 10  and in following figures that include various examples of operational flows, discussion and explanation may be provided with respect to the above-described examples of  FIGS. 1-9 , and/or with respect to other examples and contexts. However, it should be understood that the operational flows may be executed in a number of other environments and contexts, and/or in modified versions of  FIGS. 1-9 . Also, although the various operational flows are presented in the sequence(s) illustrated, it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently.  
      After a start operation, the operational flow  1000  moves to an accessing operation  1010  where at least one dataset may be accessed, based on at least one treatment parameter and at least one predictive basis. For example, as shown in  FIG. 9 , at least one dataset may be accessed from treatment data  126 , based on at least one treatment parameter and at least one predictive basis that are received from/through the submission element  902  of the user interface  132 , where the submission element  902  may include, for example, one or more of the fields  804 - 814 . For example, the at least one treatment parameter and the at least one predictive basis may be received in association with a request that is received from the user interface  132  at the event handler  906  and forwarded to the dataset view logic  908  within the treatment logic  128 . In one example, such a request may include a treatment agent “radionuclides” specified in the field  806 , as well as a predictive basis (e.g., “human trials”) specified in the field  812 . Accordingly, and as described herein (e.g., with reference to  FIG. 9 ), the dataset view logic  908  may thus communicate/interface with the DBMS engine  130  to access the treatment data  126  and obtain the (corresponding) at least one dataset therefrom. In other example implementations, the at least one treatment parameter and the at least one predictive basis may be acquired from a source other than the user interface  132 . For example, the at least one treatment parameter and the at least one predictive basis may be received as part of, or in association with, a currently-performed procedure, and/or may be acquired from instruments measuring data related to the currently-performed procedure, for example, for a course of treatment of the patient  106  (so that, for example, such data may include or be associated with the at least one treatment parameter and the at least one predictive basis, or such data may be received as part of a process for generating the at least one treatment parameter and the at least one predictive basis).  
      In some implementations, in accessing the at least one dataset based on the at least one treatment parameter, the at least one dataset may be accessed based on the at least one treatment parameter and on at least one associated parameter of the treatment parameter. For example, the at least one dataset may be accessed based on an associated parameter that may include, for example, a diagnostic, symptomatic, screening, preventative, and/or research parameter(s) that may be correlated, e.g., in the treatment data  126  and/or by the treatment logic  128 , with a treatment parameter(s). As a result, for example, the clinician  104  may specify such an associated parameter using the user interface  132 , and may thereafter be provided with a suggested course of action (and/or possible outcome thereof), without having to specify (or otherwise have knowledge of) the at least one treatment parameter.  
      As a more specific example of the associated parameter just referenced, an inflammation marker may be used to diagnose or recognize an increased risk of certain diseases (e.g., heart disease). Such parameters, in a diagnostic setting, may lead to a diagnosis indicating use of a corresponding treatment parameter(s) to achieve a desired effect (e.g., a corresponding anti-inflammatory treatment agent, which may be delivered to an appropriate bodily location(s) by way of appropriate target-related tissue-ancestry correlated binding site(s), associated target-related tissue-ancestry correlated binding agent(s), and/or treatment agent(s)). In other words, for example, the treatment system  102  may determine the at least one treatment parameter based on the at least one associated parameter (e.g., the inflammation marker, which may be received from the clinician  104  through the user interface  132 ), and may then access the at least one dataset based on the at least one treatment parameter (and the at least one predictive basis).  
      Thus, for example, it should be understood that the user interface  132  and/or the treatment system  102  may be used by the clinician  104  with little or no external reference to the treatment parameters/treatment characteristics  302 - 320  of  FIGS. 3-7  being visible to, or directly used by, the clinician  104 . For example, the user interface  132  may present (and/or allow the clinician  104  to specify) a particular illness, and corresponding (suggested) medical procedure(s), where such illness(es) and procedures may be related/applicable to one another through application of, for example, appropriately-selected target-related tissue-ancestry correlated binding site(s) and target-related tissue-ancestry correlated binding agent(s). In such cases, then, the treatment system  102  acts transparently, so that the clinician  104  need not consider, or even be aware of, these particular mechanisms underlying the suggested procedure(s), and, instead, may simply be provided by the user interface  132  with a suggested procedure (and indicated efficacy thereof) for a specified illness.  
      Then, in a determining operation  1020 , a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion may be determined, based on the at least one dataset. For example, the dataset view logic  908  may determine the first possible outcome of a use of the treatment agent specified in the field  806 , based on the at least one dataset, where the first possible outcome may include, for example, an efficacy of the treatment agent on a direct end target as the at least one body portion (e.g., on “cancer cells in lung,” as may be specified in the field  804 ), and/or a risk, side effect, or consequence of the treatment agent on the at least one body portion, or on at least one other body portion (e.g., the pancreas  110 ). Then, continuing the example, the dataset view logic  908  may determine, perhaps in conjunction with the display update logic  912 , the graphical illustration, e.g., the graphical illustration  802  of at least a portion of a human body in which the lungs  108  and/or pancreas  110  (or portions thereof) are included. In determining the graphical illustration, the various effects described herein may be employed, e.g., coloring, highlighting, or otherwise visually indicating the at least one body portion in order to indicate or convey the first possible outcome (e.g., a degree of brightness corresponding to a degree of efficacy or risk associated with the treatment agent). As should be apparent, then, the graphical illustration  802  may be provided using the user interface  132 , e.g., by updating the display element  904  accordingly.  
      As described herein, the first possible outcome may thus be based on at least one dataset that may be specified, for example, using the field  812 , where the at least one predictive basis may correspond to the at least one dataset and may be pre-configured, defined, or characterized (e.g., as being either relatively more or less predictively useful than a comparison dataset). Although the graphical illustration  802  includes specific body portions such as the lungs  108  and pancreas  110 , it should be understood that the graphical illustration  802  may be determined with respect to an entire body of the patient  106  (e.g., where the treatment parameter includes a blood pressure or other characteristic of the patient  106  that is not localized to a particular body portion), and/or may be determined as an additional or alternative representation of data (e.g., as a blood pressure chart illustrated along with, or as some or all of, the graphical illustration  802 ).  
      In an applying operation  1030 , a filter criteria may be applied to the at least one dataset to obtain a filtered dataset. For example, a filter criteria may be received at the filter logic  910  from the submission element  902  (e.g., by way of the event handler  906 ), where the submission element  902  may include the field  814 . In this way, the filter criteria may be selected, e.g., by the clinician  104 , for application to the at least one dataset. For example, in a case where the at least one dataset includes an “aggregation” of available datasets (as specified using the field  812 ), so that the graphical illustration  802  is provided based thereon, then a filter criteria of “computer simulation” may be selected in the field  814 . Accordingly, the filter logic  910  may apply the filter criteria to the at least one (e.g., the “aggregation”) dataset, to remove the dataset/predictive basis of “computer simulation” therefrom and obtain a resulting, filtered dataset.  
      Then, in a determining operation  1040 , a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined, based on the filtered dataset. For example, and continuing the example(s) just given with respect to one or more of the operation(s)  1010 - 1030 , the filter logic  910  (perhaps in conjunction with the dataset view logic  908  and/or the display update logic  912 ) may provide a modified version of the graphical illustration  802 , illustrating a second possible outcome as including, for example, an efficacy of the treatment agent (e.g., “radionuclides”) on the at least one body portion (e.g., the direct end target “cancer cells in lung”), as predicted by the filtered dataset and any associated predictive bases (e.g., based on the filtered dataset of “aggregation but with ‘computer simulation’ removed”). Then, the modified graphical illustration (e.g., a modified version of the graphical illustration  802 ) may be determined, including a corresponding/modified altering, coloring, or indicating of the at least one body portion. For example, such use and application of the filter criteria by the filter logic  910  may thus result in altering the graphical illustration  802  or some part thereof, including adding new or additional aspects of the graphical illustration  802 , or replacing some or all of the graphical illustration  802 , e.g., in conjunction with any of the techniques described above with respect to the providing operation  1010 , or with other techniques described herein, including, for example, the use of visual indicators and/or illustrated/highlighted body portions.  
      As a result of the operations  1010 - 1040 , for example, the clinician  104  may view the first possible outcome as predicted using the combined predictive bases of all the datasets of the field  812  (e.g., by selecting “aggregation” in the field  812 ), and as illustrated in the graphical illustration  802 . The clinician  104  may subsequently view the second possible outcome as predicted using all of the above-described combined predictive bases, but with an effect of the predictive basis associated with the “computer simulation” dataset having been removed for illustration of the modified version of the graphical illustration  802 . In this way, and as described in more detail herein, the clinician  104  may view a range of possible outcomes, based on a number of different types of predictive bases, where the various predictive bases may be characterized as being more or less predictively useful relative to one another. Accordingly, an ability of the clinician  104  to diagnose and treat patients, and/or to perform clinical research, may be improved.  
      In this regard, it should be understood that the operation(s)  1010 - 1040  may be performed with respect to a digital representation (e.g., as digital data), for example, of the treatment parameter, the dataset(s), and/or the filter criteria (e.g., the filter criteria of the field  814 ). For example, as may be understood with reference to  FIGS. 9 and 10 , the treatment logic  128  may accept a digital or analog (for conversion into digital) representation of the at least one treatment parameter from the user interface  132  (e.g., from the submission element  902 ), for presentation to the DBMS engine  130  and/or the treatment data  126 . As another example, the treatment logic  128  may provide a digitally-encoded representation of the graphical illustration  802 , or a modified version thereof, based on the treatment data  126 , where the treatment data  126  may be implemented and accessed locally, and/or may be implemented and accessed remotely.  
      Thus, an operation(s) may be performed related either to a local or remote storage of the digital data, or to another type of transmission of the digital data. As discussed herein, in addition to accessing, querying, recalling, or otherwise obtaining the digital data for the providing operation, operations may be performed related to storing, assigning, associating, or otherwise archiving the digital data to a memory, including, for example, sending and/or receiving a transmission of the digital data from a remote memory. Accordingly, any such operation(s) may involve elements including at least an operator (e.g., either human or computer) directing the operation, a transmitting computer, and/or a receiving computer, and should be understood to occur within the United States as long as at least one of these elements resides in the United States.  
       FIG. 11  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 11  illustrates example embodiments where the accessing operation  1010  may include at least one additional operation. Additional operations may include operation  1102 , operation  1104 , operation  1106 , operation  1108 , operation  1110 , operation  1112 , and/or operation  1114 .  
      At the operation  1102 , the at least one treatment parameter and the at least one predictive basis may be received in association with at least one request received from a graphical user interface. For example, the at least one treatment parameter and the at least one predictive basis may be received in association with a request received from the user interface  132  (e.g., as entered or selected by the clinician  104 ). Then, at the operation  1104 , the at least one dataset may be accessed in response to the at least one request. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset from the treatment data  126 .  
      At the operation  1106 , the at least one treatment parameter and the at least one predictive basis may be received from at least one submission element of a graphical user interface. For example, the at least one treatment parameter and the at least one predictive basis may be received from the submission element  902  of the user interface  132 , where the submission element  902  may include, for example, one or more of the fields  804 - 814 . Then, at the operation  1108 , the at least one dataset may be accessed based on the at least one treatment parameter and the at least one predictive basis. For example, as described herein, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset from the treatment data  126 .  
      At the operation  1110 , the at least one dataset may be accessed by being taken from treatment data associated with a plurality of treatment parameters and predictive bases. For example, the at least one dataset may be accessed from within the treatment data  126 , which, as described herein, may include treatment data such as is also described herein, for example, with respect to  FIGS. 5-7 , where various examples of treatment parameters  302 - 320  (and instances thereof) are provided (and where, as noted herein, the various examples of treatment parameters  302 - 320  also may be referred to as, or considered to be, treatment characteristics or treatment information). Elements of the treatment data  126  may be associated with one or more predictive bases, for accessing of the at least one dataset based thereon, e.g., by, associating elements of the treatment data with one or more of the tag(s)  918 ,  920 . In this way, the dataset view logic  908  may correspond the at least one predictive basis with elements of the treatment data  126 , i.e., with elements of the at least one dataset.  
      At the operation  1112 , the at least one dataset may be accessed by corresponding the at least one predictive basis with at least one tag associated with at least one element of the at least one dataset. For example, and as just referenced, the dataset view logic  908  may correspond the at least one predictive basis with one or more of the tag(s)  918 ,  920 , which may include, for example, XML tags associated with element(s) of the treatment data  126 , thereby to identify, and thus access, one or more data element(s) that may then be aggregated into the at least one dataset.  
      At the operation  1114 , the at least one dataset may be accessed by structuring a query of a database, based on the at least one treatment parameter and the at least one predictive basis. For example, the dataset view logic  908  may interact/interface with the DBMS engine  130  (e.g., with the query generator  914 ) to structure a query of the treatment data  126 . For example, SQL or SQL-like operations using the specific (instance of the) at least one treatment parameter may be performed, and/or Boolean operations using the at least one treatment parameter and the at least one predictive basis may be performed.  
       FIG. 12  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 12  illustrates example embodiments where the accessing operation  1010  may include at least one additional operation. Additional operations may include operation  1202 , operation  1204 , operation  1206 , operation  1208 , and/or operation  1210 .  
      At the operation  1202 , the at least one dataset may be accessed using a database management system engine that is configured to query a database to retrieve the at least one dataset therefrom. For example, the DBMS engine  130  may be used to query a relational or object-oriented database including the treatment data  126 , so that elements of the treatment data  126  may be identified, retrieved, and/or aggregated as the at least one dataset.  
      At the operation  1204 , the at least one dataset may be accessed by corresponding the at least one predictive basis including at least one actual and/or theoretical analysis of the use of the at least one treatment parameter with at least one element of the at least one dataset. For example, the dataset view logic  908  may access the treatment data  126  based on the at least one treatment parameter and the at least one predictive basis, where the at least one treatment parameter and the at least one predictive basis may be received from the user interface  132  based on a selection of one of the values of the fields  804 - 810  and/or  812 , so that the at least one predictive basis includes or is associated with an actual analysis (e.g., human studies or animal studies, or any in vivo or in vitro study) and/or a theoretical analysis (e.g., in silico and/or computer simulations, or speculation).  
      At the operation  1206 , the at least one dataset may be accessed by corresponding the at least one predictive basis, including one or more of a human study, an animal study, a computer simulation, a speculation, and/or a professionally-informed speculation, with at least one data element of the at least one dataset. For example, the dataset view logic  908  may correspond the at least one predictive basis, as received from the field  812 , with at least one element of the at least one dataset as accessed from the treatment data  126 , where the field  812  is used to specify human studies, animal studies, or any of the other predictive bases included therein, or other predictive bases that may be provided, or combinations thereof.  
      At the operation  1208 , the at least one dataset may be accessed as being associated with the at least one predictive basis, based on a characterization stored in association with each element of the at least one dataset and related to one or more of a source of the at least one dataset, a funding of the at least one dataset, a procedural aspect of the at least one dataset, a source of support associated with the at least one dataset, a research field of the at least one dataset, a time period or time interval of collection of the at least one dataset, a professional publication associated with the at least one dataset, a professional author or investigator associated with the at least one dataset, or a location of collection of the at least one dataset. For example, the at least one dataset may be characterized by a source of funding of the research that supplied the results of the at least one dataset, where, for example, a certain funding source may be associated with a higher (net) predictive value than others. Similarly, a research field associated with the at least one dataset (e.g., oncology or hematology) may be associated with, or characterized as, having a greater or lesser predictive value, e.g., through use of the tags  918 ,  920 , or using other data characterization techniques.  
      At the operation  1210 , the at least one dataset may be accessed based on the at least one predictive basis and at least one other predictive basis. For example, the dataset view logic  908  may access the at least one dataset specified in the field  812  (e.g., from the treatment data  126 ) as being associated with the at least one predictive basis, such as, for example, “human studies,” where results from at least one other dataset may be included in the at least one dataset, but having another predictive basis, such as “animal studies.” In other words, results from different studies, datasets, and/or predictive bases may be combined when accessing the at least one dataset, so that, as described herein, the clinician  104  may consider such combinations when deciding on a diagnosis, treatment, or course of research. In this regard, and as described in more detail herein, it should be understood that in many cases, a predictive basis of “human studies” may be assumed to be more predictively useful than a predictive basis of “animal studies” in the context of deciding diagnosis, treatment, or research for human patients. More generally, however, a predictive basis and/or a relative predictive value thereof may be assigned or associated with results, data, or datasets within the treatment data  126 , prior to a use of the user interface  132  by the clinician  104 , using, e.g., the tags  918  and  920 , or similar techniques. That is, in some implementations, different predictive bases may be objectively and verifiably designated as having a defined relative value of predictive usefulness (e.g., relative to one another). Accordingly, one skilled in the art would appreciate that no subjectivity is involved in providing the graphical illustration  802  (as described herein) based on the different predictive bases, as those predictive bases may be provided in associated software, hardware, and/or firmware. Of course, the graphical illustration  802  may nonetheless have more or less subjective value to the clinician  104 , based on a personal value or judgment of the clinician  104 . Moreover, in some implementations, characterizations of the predictive bases may be universal through the treatment data  126 , so that, for example, all human studies of a certain type are associated with a first predictive basis or value. In other implementations, such characterizations may be assigned by, or determined for, individual clinicians. For example, different clinicians may assign different predictive values to different (types of) datasets. In still other implementations, an artificial intelligence engine may be used to make semantic decisions regarding assessment(s) of the relative predictive value or usefulness of the different predictive bases.  
       FIG. 13  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 13  illustrates example embodiments where the accessing operation  1010  may include at least one additional operation. Additional operations may include operation  1302 , operation  1304 , operation  1306 , operation  1308 , operation  1310 , operation  1312 , operation  1314 , and/or operation  1316 .  
      At the operation  1302 , at least one target-related tissue ancestry-correlated binding site may be received as the at least one treatment parameter. For example, the dataset view logic  908  may receive the at least one treatment parameter from the user interface  132  (e.g., from a field similar to the fields  804 - 810 , or through another submission element  902 , wherein the at least one treatment parameter includes, for example, at least one protein induced and/or expressed at an interface (e.g., the endothelial layer  118 ) between tissue and/or blood and/or a blood component in the vicinity of the at least one body portion as the at least one target-related tissue ancestry-correlated binding site. Then, at the operation  1304 , the at least one dataset may be accessed including the at least one target-related tissue ancestry-correlated binding site. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one target-related tissue ancestry-correlated binding site, from the treatment data  126 .  
      At the operation  1306 , at least one target-related tissue ancestry-correlated binding agent may be received as the at least one treatment parameter. For example, the dataset view logic  908  may receive the at least one treatment parameter from the user interface  132  (e.g., by way of the field(s)  808  and/or  810 ). The at least one target-related tissue ancestry-correlated binding agent may include, for example, an I-labeled monoclonal antibody that is known to target and bind to a corresponding target-related tissue ancestry-correlated binding site. Then, at the operation  1308 , the at least one dataset may be accessed including the at least one target-related tissue ancestry-correlated binding agent. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one target-related tissue ancestry-correlated binding agent, from the treatment data  126 .  
      At the operation  1310 , at least one direct end target may be received as the at least one treatment parameter. For example, the dataset view logic  908  may receive the at least one treatment parameter from the user interface  132  (e.g., from the field  804 ). For example, the direct end target may include the lungs  108 , and/or cancerous cells thereof, as the at least one direct end target. Then, at the operation  1312 , the at least one dataset may be accessed including the at least one direct end target. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one direct end target, from the treatment data  126 .  
      At the operation  1314 , at least one discriminated end target may be received as the at least one treatment parameter. For example, although not illustrated in  FIG. 8 , the dataset view logic  908  may receive the at least one treatment parameter, including an identification of the discriminated end target as the treatment parameter, from a corresponding field of the user interface  132  (not illustrated in  FIG. 8 ). For example, the discriminated end target may include non-lung tissue/organ(s) (e.g., the pancreas  110 ), and/or non-cancerous lung tissue. Then, at the operation  1316 , the at least one dataset may be accessed including the at least one discriminated end target. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one discriminated end target, from the treatment data  126 .  
       FIG. 14  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 14  illustrates example embodiments where the accessing operation  1010  may include at least one additional operation. Additional operations may include operation  1402 , operation  1404 , operation  1406 , operation  1408 , operation  1410 , operation  1412 , operation  1414 , and/or operation  1416 .  
      At the operation  1402 , at least one direct intermediate target may be received as the at least one treatment parameter. For example, the direct intermediate target may be specified using a field (not shown) of  FIG. 8 . For example, the direct intermediate target may include endothelial tissue proximate to (e.g., cancerous) lung tissue. Then, at the operation  1404 , the at least one dataset may be accessed including the at least one direct intermediate target. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one direct intermediate target, from the treatment data  126 .  
      At the operation  1406 , at least one discriminated intermediate target may be received as the at least one treatment parameter. For example, the discriminated intermediate target may be specified using a field (not shown) of  FIG. 8 . For example, the discriminated intermediate target may include endothelial tissue proximate to non-lung tissue (e.g., endothelial tissue proximate to the pancreas  110 ). Then, at the operation  1408 , the at least one dataset may be accessed including the at least one discriminated intermediate target. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one discriminated intermediate target, from the treatment data  126 .  
      At the operation  1410 , at least one treatment agent may be received as the at least one treatment parameter. For example, the at least one treatment agent may be received at the dataset view logic  908  by way of the field  806 . For example, the treatment agent may include radionuclides that are associated with cancer cells in the lung through a desired course of treatment. Then, at the operation  1412 , the at least one dataset may be accessed including the at least one treatment agent. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one treatment agent, from the treatment data  126 .  
      At the operation  1414 , at least one treatment agent precursor may be received as the at least one treatment parameter. For example, the treatment agent precursor may be specified using a field (not shown) of  FIG. 8 . For example, the treatment agent precursor may include an agent used to facilitate application of a treatment agent, e.g., an immune-response element that is used to identify/mark/bond with a target-related tissue ancestry-correlated binding site and/or a substance that when metabolized becomes the treatment agent, such as with pro-drugs. Then, at the operation  1416 , the at least one dataset may be accessed including the at least one treatment agent precursor. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset, including the at least one treatment agent precursor, from the treatment data  126 .  
       FIG. 15  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 15  illustrates example embodiments where the determining operation  1020  may include at least one additional operation. Additional operations may include operation  1502 , operation  1504 , operation  1506 , operation  1508 , operation  1510 , operation  1512 , and/or operation  1514 .  
      At the operation  1502 , the graphical illustration may be determined for inclusion in a display element of a graphical user interface, based on the at least one dataset. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  for inclusion in the display element  904  of the graphical user interface  132 , based on a selection of “human trials” in the field  812 .  
      At the operation  1504 , an analysis of one or more aggregated elements of the at least one dataset may be performed to determine the first possible outcome of the use of the at least one treatment parameter on the at least one body portion. For example, as described herein, the first possible outcome may include an efficacy of the binding agent of the field(s)  808 / 810  of  FIG. 8  in delivering the treatment agent “radionuclides” specified in the field  806  to the direct end target “cancer cells in lung” specified in the field  804  (and/or an efficacy of the radionuclides in reducing or destroying the cancer cells). The first possible outcome may be determined based on, for example, aggregated elements of the at least one dataset. For example, if the at least one predictive basis includes “human trials,” the at least one dataset may include data elements derived from a plurality of human trials, so that the dataset view logic  908  may be required to aggregate the data elements accordingly, so that an analysis thereof may be performed to derive the first possible outcome. Similarly, the at least one predictive basis may include “human trials” and “animal studies,” so that the at least one dataset may include data elements derived from a plurality of human trials and a plurality of animal studies. Again, the dataset view logic  908  may be required to aggregate all such data elements accordingly, and perform a corresponding analysis thereof, to derive the first possible outcome. Then, at the operation  1506 , the graphical illustration may be determined, based on the analysis. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including the lungs  108 , where the lungs  108  and/or a portion thereof may be visually highlighted within the graphical illustration  802 , perhaps with a color or degree of intensity determined to correspond to the determined efficacy of the treatment agent.  
      At the operation  1508 , an analysis of one or more aggregated elements of the at least one dataset may be performed to determine the first possible outcome, the first possible outcome including a desired healing, enhancing, improving, or mitigating effect of the use of the at least one treatment parameter. For example, as just described, elements of the at least one dataset may be aggregated by the dataset view logic  908 , and an analysis thereof may be performed to determine the first possible outcome, including, for example, an improving of the lungs  108  by reduction or removal of the cancer cells therefrom. Then, at the operation  1510 , the graphical illustration may be determined, based on the analysis. For example, as just described, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including the lungs  108 , where the lungs  108  and/or a portion thereof may be visually highlighted within the graphical illustration  802 , perhaps, for example, with a color or degree of intensity determined to correspond to the determined efficacy of the treatment agent.  
      At the operation  1512 , an analysis of one or more aggregated elements of the at least one dataset may be performed to determine the first possible outcome, the first possible outcome including an undesired risk, side effect, or consequence of the use of the at least one treatment parameter. For example, the dataset view logic  908  may perform an analysis of aggregated elements of the at least one dataset as accessed from the treatment data  126 , to determine an undesired side effect (e.g., damage to the pancreas  110 ) of the use of the treatment parameter (e.g., the treatment agent “radionuclides”). Then, at the operation  1514 , the graphical illustration may be determined, based on the analysis. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including the pancreas  110 , where the pancreas  110  and/or a portion thereof may be visually highlighted within the graphical illustration  802 .  
       FIG. 16  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 16  illustrates example embodiments where the determining operation  1020  may include at least one additional operation. Additional operations may include operation  1602 , operation  1604 , operation  1606 , and/or operation  1608 .  
      At the operation  1602 , the graphical illustration may be determined including the at least one body portion in association with a visual indicator related to the first possible outcome. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including the lungs  108 , pancreas  110 , or other body portion, where the mere inclusion of such a body portion may be considered to be a visual indicator related to the first possible outcome (e.g., where the pancreas  110  is illustrated only when it is determined that possible side effects may be associated with the pancreas  110  when using the lungs  108  (or cancerous cells therein) as a direct end target). In other implementations, and as referenced herein, the visual indicator may include a coloring, highlighting, designating, marking, identifying shading, cross-hatching, flashing, or other visual effect. In such examples, the visual indicator(s) may be related to, or indicate, the first possible outcome, e.g., the efficacy (or risks, or unwanted consequences) of one or more (combinations of) treatment parameters. For example, the graphical illustration  802  or appropriate portion(s) thereof may have its color changed, or may be highlighted or otherwise marked/designated to indicate a level of efficacy of selected treatment parameter(s). For example, an efficacy of each treatment parameter may be shown individually or together, since, for example, an efficacy of the target-related, tissue ancestry-correlated binding agent of the field  808  may refer to an ability of such an agent to deliver any treatment agent to (a corresponding target-related, tissue ancestry-correlated binding site within) a direct end target of the field  804 , irrespective of which treatment agent is associated therewith. Meanwhile, an efficacy of the treatment agent of the field  806  may refer to an actual treatment result (e.g., reduction or destruction of cancer cells), and, in another example, an efficacy of the combination of treatment parameters may refer to an overall success of the treatment, including management or reduction of associated risks and side effects.  
      At the operation  1604 , the graphical illustration may be determined including at least one other body portion in association with a visual indicator related to the first possible outcome. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including the pancreas  110  as the at least one other body portion, when, for example, the lungs  108  may be included as the at least one body portion.  
      At the operation  1606 , a correlation may be determined between the first possible outcome and a type and/or characteristic of a visual indicator used in the graphical illustration to represent the first possible outcome. For example, the dataset view logic  908  and/or the display update logic  912  may determine that the first possible outcome includes a particularly effective (or ineffective) use of the treatment agent “radionuclides” in treating the direct end target “cancer cells in lung.” In this case, the dataset view logic  908  and/or the display update logic  912  also may determine how this first possible outcome may be included in the graphical illustration  802 , e.g., using any of the visual indicators described herein. As should be apparent, such different types of visual indicators may be specified, for example, by a user preference of the clinician  104  (e.g., a first clinician may wish to see a particularly effective treatment illustrated using a specified color to illustrate the direct end target in the graphical illustration  802 , while a second clinician may wish to see such an outcome illustrated by increasing a brightness of the direct end target).  
      At the operation  1608 , the graphical illustration of the first possible outcome of the use of the at least one treatment parameter may be determined, the at least one treatment parameter including one or more of at least one target-related tissue ancestry-correlated binding site; at least one target-related tissue ancestry-correlated binding agent, at least one direct end target, at least one discriminated end target, at least one direct intermediate target, at least one discriminated intermediate target, at least one treatment agent delivery mechanism relative to the at least one target-related tissue ancestry-correlated binding agent, at least one treatment agent, or at least one treatment agent precursor. For example, the dataset view logic  908  and/or the display update logic  912  may determine the graphical illustration  802  including an illustration of a first possible outcome of the use of one or more of a direct end target, a treatment agent, or a target-related tissue ancestry-correlated binding agent, as these or other examples of the operation  1402  may be selected, provided, or otherwise specified, using the fields  804 - 810 , or similar fields.  
       FIG. 17  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 17  illustrates example embodiments where the applying operation  1030  may include at least one additional operation. Additional operations may include operation  1702 , operation  1704 , operation  1706 , operation  1708 , operation  1710 , operation  1712 , operation  1714 , operation  1716 , operation  1718 , and/or operation  1720 .  
      At the operation  1702 , the filter criteria may be received from a submission element of a graphical user interface. For example, the treatment logic  128  may receive the filter criteria (e.g., “computer simulation”) from the submission element  902  (e.g., the field  814 ) of the user interface  132 . At the operation  1704 , the filter criteria may be applied to the at least one dataset. For example, the filter logic  910  may apply the filter criteria to the at least one dataset.  
      At the operation  1706 , a query of treatment data may be formulated, the query specifying the filter criteria and the at least one dataset. For example, the filter logic  910  may formulate a query of the treatment data  126 , e.g., in conjunction with the query generator  914  of the DBMS engine  130 . The query may specify the filter criteria (e.g., “computer simulation” as specified in the field  814 ), as well as the at least one dataset (e.g., “aggregation” as specified in the field  812 ). At the operation  1708 , the filter criteria may be applied to the at least one dataset. For example, the filter logic  901  may apply the filter criteria “computer simulation,” in response to the query formulated by the filter logic  910 . In this way, as described herein, and for example, the clinician  104  may limit a prediction of the first possible outcome to remove/exclude undesired data in predicting the second possible outcome.  
      At the operation  1710 , at least one data element within the at least one dataset that is associated with the filter criteria may be determined. For example, the filter logic  910  may associate the filter criteria “computer simulation” with at least one data element within the at least one dataset “aggregation.” At the operation  1712 , the filter criteria may be applied to remove the at least one data element from the at least one dataset. For example, the filter logic  910  may remove the data element associated with the filter criteria “computer simulation” from the “aggregation” dataset.  
      At the operation  1714 , at least one data element within at least a first dataset and at least a second dataset of the at least one dataset may be determined, the at least one data element associated with the filter criteria. For example, the filter logic  910  may determine that the filter criteria includes “conducted in region x,” as specified in the field  814 . That is, for example, and as explained herein, the clinician  104  may specify both “human trials” and “animal studies” in the field  814 , but may feel that either of these predictive bases should be considered unreliable if conducted in a particular geographical region (e.g., in a particular country). Accordingly, the filter logic  910  may determine a data element within a first dataset associated with both the filter criteria “conducted in region x” and the predictive basis “human trials,” and a data element within a second dataset associated with both the filter criteria “conducted in region x” and the predictive basis “animal studies,” e.g., by accessing the treatment data  126 . At the operation  1716 , the filter criteria may be applied to remove the at least one data element from the at least the first dataset and/or the at least the second dataset and obtain the filtered dataset. For example, the filter logic  910  may apply the filter criteria to remove the data elements just described (e.g., in the first dataset and associated with “human trials”/” conducted in region x” and “animal studies”/” conducted in region x” may be removed) to obtain the remaining, filtered dataset.  
      At the operation  1718 , the at least one data element may be located by corresponding the filter criteria with a tag associated with the at least one data element of the at least one dataset. For example, the filter logic  910  may associate the filter criteria “computer simulation” with tag(s)  918 ,  920  associated with one or more data element of the dataset “aggregation.” Then, at the operation  1720 , the filter criteria may be applied to remove the at least one data element from the at least one dataset. For example, the filter logic  910  may apply the filter criteria “computer simulation” to remove the corresponding (e.g., tagged) data element of the dataset “aggregation.”  
       FIG. 18  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 18  illustrates example embodiments where the applying operation  1030  may include at least one additional operation. Additional operations may include operation  1802 , operation  1804 , operation  1806 , operation  1808 , and/or operation  1810 .  
      At the operation  1802 , the filter criteria may be applied to the at least one dataset by associating at least one data element of the at least one dataset with the filter criteria, the filter criteria including one or more of a human study, an animal study, a computer simulation, a speculation, a professionally-informed speculation, a type of human study, a type of animal study, a type of computer simulation, a type of speculation, and/or a type of professionally-informed speculation. For example, the filter logic  910  may apply the filter criteria “human trials” as specified in the field  814  to the at least one dataset (e.g., the dataset “aggregation” specified in the field  812 ).  
      At the operation  1804 , the filter criteria may be applied to at least a first dataset and a second dataset of the at least one dataset, the at least the first dataset and the at least the second dataset being associated with a first predictive basis and a second predictive basis, respectively, of the at least one predictive basis. For example, the filter logic  910  may apply the filter criteria “using protocol x” as specified in the field  814  to a first dataset (e.g., a dataset associated with “human trials” in the field  812 ) and a second dataset (e.g., the dataset “animal studies” specified in the field  812 ).  
      At the operation  1806 , the filter criteria may be applied to the at least one dataset to associate a data element of the at least one dataset with the filter criteria, the filter criteria including one or more of a source of the at least one dataset, a funding of the at least one dataset, a research field of the at least one dataset, a time period of collection of the at least one dataset, or a location of collection of the at least one dataset. For example, the filter logic  910  may apply a filter criteria associated with a source of funding of the at least one dataset, as specified in the field  814  (although not specifically illustrated in the example of  FIG. 9 ) with at least one data element of the at least one dataset (e.g., “aggregation”), as specified in the field  812 .  
      At the operation  1808 , at least one data element of the at least one dataset may be associated with a first predictive basis of the at least one predictive basis, the at least one predictive basis including at least the first predictive basis and a second predictive basis. For example, the filter logic  910  may associate at least one data element of a dataset including both “human trials” and “animal studies,” both being specified in the field  812 , with a first predictive basis “human trials” of the two (e.g., first and second) predictive bases. Then, at the operation  1810 , the filter criteria may be applied to remove the at least one data element from the at least one dataset. For example, the filter logic  910  may apply the filter criteria “human trials” to remove the at least one data element associated therewith from the at least one dataset.  
       FIG. 19  illustrates alternative embodiments of the example operational flow  1000  of  FIG. 10 .  FIG. 19  illustrates example embodiments where the determining operation  1040  may include at least one additional operation. Additional operations may include operation  1902 , operation  1904 , operation  1906 , operation  1908 , operation  1910 , operation  1912 , and/or operation  1914 .  
      At the operation  1902 , the modified graphical illustration may be determined for inclusion thereof in a display element of a graphical user interface. For example, based on an output of the filter logic  910 , the display update logic  912  may determine the modified graphical illustration to replace the graphical illustration  802 , using the display element  904  and using any of the determining operations/techniques described herein, e.g., with respect to the operations  1502 - 1514 , and/or other determining operations/techniques.  
      At the operation  1904 , an analysis of one or more aggregated elements of the filtered dataset may be performed. For example, the filter logic may perform an analysis of aggregated elements of the filtered dataset, as accessed from the treatment data  126 . At the operation  1906 , the modified graphical illustration may be determined based on the analysis. For example, the dataset view logic  908  and/or the display update logic  912  may determine the modified graphical illustration for inclusion in/with the display element  904  based on the analysis, so that the modified graphical illustration illustrates or otherwise conveys or includes a prediction associated with the filtered dataset in the second possible outcome.  
      At the operation  1908 , a visual indicator associated with the at least one body portion and/or at least one other body portion may be determined, based on the filtered dataset. For example, the dataset view logic  908  and/or the display update logic  912  may determine that a particular color may be used as the visual indicator and associated with the lung(s)  108  and/or the pancreas  110 . At the operation  1910 , the modified graphical illustration may be determined, including the visual indicator. For example, the dataset view logic  908  and/or the filter logic  910  may determine a modified version of the graphical illustration  802 , by changing a color of the lung(s)  108  and/or the pancreas  110 .  
      At the operation  1912 , the modified graphical illustration may be determined including an indication of a potential efficacy of the use of the at least one treatment parameter. For example, a shade of a color used in providing the lung(s)  108  may be altered to provide the indication of the potential efficacy of the use of the treatment agent “radionuclides.” 
      At the operation  1914 , the modified graphical illustration may be determined including an indication of a potential side effect and/or risk of the use of the at least one treatment parameter. For example, a shade of a color used in providing the pancreas  110  may be altered to provide the indication of the side effect of the use of the treatment agent “radionuclides.” 
       FIG. 20  illustrates a partial view of an example computer program product  2000  that includes a computer program  2004  for executing a computer process on a computing device. An embodiment of the example computer program product  2000  is provided using a signal bearing medium  2002 , and may include at least one of one or more instructions for accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, the signal bearing medium also bearing one or more instructions for determining a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, the signal bearing medium also bearing one or more instructions for applying a filter criteria to the at least one dataset to obtain a filtered dataset, and the signal bearing medium also bearing one or more instructions for determining a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. The one or more instructions may be, for example, computer executable and/or logic-implemented instructions. In one implementation, the signal-bearing medium  2002  may include a computer-readable medium  2006 . In one implementation, the signal bearing medium  2002  may include a recordable medium  2008 . In one implementation, the signal bearing medium  2002  may include a communications medium  2010 .  
       FIG. 21  illustrates an example system  2100  in which embodiments may be implemented. The system  2100  includes a computing system environment. The system  2100  also illustrates the clinician  104  using a device  2104 , which is optionally shown as being in communication with a computing device  2102  by way of an optional coupling  2106 . The optional coupling  2106  may represent a local, wide-area, or peer-to-peer network, or may represent a bus that is internal to a computing device (e.g., in example embodiments in which the computing device  2102  is contained in whole or in part within the device  2104 ). A storage medium  2108  may be any computer storage media.  
      The computing device  2102  includes computer-executable instructions  2110  that when executed on the computing device  2102  cause the computing device  2102  to access at least one dataset, based on at least one treatment parameter and at least one predictive basis, determine a graphical illustration of a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, apply a filter criteria to the at least one dataset to obtain a filtered dataset, and determine a modified graphical illustration of a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset.  
      In  FIG. 21 , then, the system  2100  includes at least one computing device (e.g.,  2102  and/or  2104 ). The computer-executable instructions  2110  may be executed on one or more of the at least one computing device. For example, the computing device  2102  may implement the computer-executable instructions  2110  and output a result to (and/or receive data from) the computing (clinician) device  2104 . Since the computing device  2102  may be wholly or partially contained within the computing (clinician) device  2104 , the computing (clinician) device  2104  also may be said to execute some or all of the computer-executable instructions  2110 , in order to be caused to perform or implement, for example, various ones of the techniques described herein, or other techniques.  
      The clinician device  2104  may include, for example, one or more of a personal digital assistant (PDA), a laptop computer, a tablet personal computer, a networked computer, a computing system comprised of a cluster of processors, a workstation computer, and/or a desktop computer. In another example embodiment, the clinician device  2104  may be operable to communicate with the computing device  2102  to communicate with a database (e.g., implemented using the storage medium  2108 ) to access the at least one dataset and/or to access the second dataset.  
       FIG. 22  illustrates an operational flow  2200  representing example operations related to filtering predictive data. After a start operation, the operational flow  2200  moves to an accessing operation  2210  where at least one dataset may be accessed, based on at least one treatment parameter and at least one predictive basis. For example, as shown in  FIG. 9 , at least one dataset may be accessed from treatment data  126 , based on at least one treatment parameter and at least one predictive basis that are received from/through the submission element  902  of the user interface  132 , where the submission element  902  may include, for example, one or more of the fields  804 - 814 .  
      Then, in a determining operation  2220 , data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion may be determined, based on the at least one dataset. For example, the dataset view logic  908  may determine the first possible outcome of a use of the treatment agent specified in the field  806 , based on the at least one dataset, where the first possible outcome may include, for example, an efficacy of the treatment agent on a direct end target as the at least one body portion (e.g., on “cancer cells in lung,” as may be specified in the field  804 ), and/or a risk, side effect, or consequence of the treatment agent on the at least one body portion, or on at least one other body portion (e.g., the pancreas  110 ). The treatment logic  128  may then determine the data associated with the first possible outcome, based on the at least one dataset.  
      In an applying operation  2230 , a filter criteria may be applied to the at least one dataset to obtain a filtered dataset. For example, a filter criteria may be received at the filter logic  910  from the submission element  902  (e.g., by way of the event handler  906 ), where the submission element  902  may include the field  814 . In this way, the filter criteria may be selected, e.g., by the clinician  104 , for application to the at least one dataset. For example, in a case where the at least one dataset includes an “aggregation” of available datasets (as specified using the field  812 ), so that the graphical illustration  802  is provided based thereon, then a filter criteria of “computer simulation” may be selected in the field  814 . Accordingly, the filter logic  910  may apply the filter criteria to the at least one (e.g., the “aggregation”) dataset, to remove the dataset/predictive basis of “computer simulation” therefrom and obtain a resulting, filtered dataset.  
      Then, in a determining operation  2240 , data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined, based on the filtered dataset. For example, and continuing the example(s) just given with respect to one or more of the operation(s)  2210 - 2230 , the filter logic  910  (perhaps in conjunction with the dataset view logic  908  and/or the display update logic  912 ) may determine the data associated with the second possible outcome, where the second possible outcome may include, for example, an efficacy of the treatment agent (e.g., “radionuclides”) on the at least one body portion (e.g., the direct end target “cancer cells in lung”), as determined from the filtered dataset.  
       FIG. 23  illustrates alternative embodiments of the example operational flow  2200  of  FIG. 22 .  FIG. 23  illustrates example embodiments where the accessing operation  2210  may include at least one additional operation. Additional operations may include operation  2302 .  
      At the operation  2302 , the at least one treatment parameter may be determined, utilizing at least one associated parameter, the at least one associated parameter including at least one diagnostic parameter, symptomatic parameter, preventative parameter, screening parameter, and/or research parameter. For example, the associated parameter may include a diagnostic parameter, such as, for example, an inflammation marker, as referenced herein, which may be used as a diagnostic parameter for diagnosing heart disease, and/or as a screening parameter for screening the patient  106  as being at risk for heart disease. Then, at the operation  2304 , the at least one dataset may be accessed, based on the at least one treatment parameter determined utilizing the at least one associated parameter. For example, the treatment logic  128 , e.g., the dataset view logic  908 , may access the at least one dataset from the treatment data  126 , by associating, for example, the diagnostic parameter with the (associated) at least one treatment parameter (e.g., a target-related tissue ancestry-correlated binding site). In this way, as described herein, the user interface  132  may require little or no knowledge of specific treatment parameters on the part of the clinician  104 , since correlation or other association between the associated parameter and the at least one treatment parameter may occur internally within the treatment system  102 .  
       FIG. 24  illustrates alternative embodiments of the example operational flow  2200  of  FIG. 22 .  FIG. 24  illustrates example embodiments where the determining operation  2220  may include at least one additional operation. Additional operations may include an operation  2402 , an operation  2404 , an operation  2406 , and/or an operation  2408 .  
      At the operation  2402 , a graphical illustration associated with the first possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, the dataset view logic  908  may determine, perhaps in conjunction with the display update logic  912 , the graphical illustration, e.g., the graphical illustration  802  of at least a portion of a human body. In determining the graphical illustration, the various effects described herein may be employed, e.g., coloring, highlighting, or otherwise visually indicating the at least one body portion in order to indicate or convey the first possible outcome (e.g., a degree of brightness corresponding to a degree of efficacy or risk associated with the treatment agent). As should be apparent, then, the graphical illustration  802  may be provided using the user interface  132 , e.g., by updating the display element  904  accordingly.  
      At the operation  2404 , an audible output associated with the first possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, the treatment logic  128  may determine a sound to be played over a speaker of the clinician device  134  (see, e.g., FIGS.  1  and/or  2 ), based on the data associated with the first possible outcome. For example, in a case where a particularly dangerous side effect exists that is associated with the first possible outcome, the clinician device  134  may sound an alarm that notifies the clinician  104  not to proceed with an associated treatment. The audible output may be output in addition to, association with, or alternatively to, the graphical illustration  802 .  
      At the operation  2406 , a vibratory alert associated with the first possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, again in a case where a particularly dangerous side effect exists that is associated with the first possible outcome, the clinician device  134  may vibrate to notify the clinician  104  not to proceed with an associated treatment. The vibratory alert may be output in addition to, association with, or alternatively to, the graphical illustration  802  and/or the audible output just referenced.  
      At the operation  2408 , a target apparatus for transmission thereto of the data associated with the first possible outcome may be determined. For example, the treatment system  102 , or a portion thereof, may be implemented on the data management system  204  of  FIG. 2 , and may transmit the data associated with the first possible outcome to the clinician device  134 , as illustrated in  FIG. 2 . In this way, for example, a memory or processing power of the clinician device  134  may be conserved.  
       FIG. 25  illustrates alternative embodiments of the example operational flow  2200  of  FIG. 22 .  FIG. 25  illustrates example embodiments where the determining operation  2240  may include at least one additional operation. Additional operations may include an operation  2502 , an operation  2504 , an operation  2506 , and/or an operation  2508 .  
      At the operation  2502 , a modified graphical illustration associated with the second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, a modified graphical illustration (e.g., a modified version of the graphical illustration  802 ) may be determined, including a corresponding/modified altering, coloring, or indicating of the at least one body portion.  
      At the operation  2504 , an audible output associated with the second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, the treatment logic  128  may determine a sound to be played over a speaker of the clinician device  134  (see, e.g., FIGS.  1  and/or  2 ). As should be understood, since the data associated with the second possible outcome may be different from the data associated with the first possible outcome (e.g., may predict a higher or lesser degree of severity of a predicted side effect), the audible output associated with the second possible outcome may be, for example, louder or softer than an audible output associated with the first possible outcome.  
      At the operation  2506 , a vibratory alert associated with the second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion may be determined. For example, the treatment system  102  may cause the clinician device  134  to vibrate to notify the clinician  104  not to proceed with an associated treatment.  
      At the operation  2508 , a target apparatus for transmission thereto of the data associated with the second possible outcome may be determined. For example, as referenced herein, the treatment system  102 , or a portion thereof, may be implemented on the data management system  204  of  FIG. 2 , and may transmit the data associated with the second possible outcome to the clinician device  134 , as illustrated in  FIG. 2 .  
       FIG. 26  illustrates a partial view of an example computer program product  2600  that includes a computer program  2604  for executing a computer process on a computing device. An embodiment of the example computer program product  2600  is provided using a signal bearing medium  2602 , and may include at least one of one or more instructions for accessing at least one dataset, based on at least one treatment parameter and at least one predictive basis, the signal bearing medium also bearing one or more instructions for determining data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, the signal bearing medium also bearing one or more instructions for applying a filter criteria to the at least one dataset to obtain a filtered dataset, and the signal bearing medium also bearing one or more instructions for determining data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset. The one or more instructions may be, for example, computer executable and/or logic-implemented instructions. In one implementation, the signal-bearing medium  2602  may include a computer-readable medium  2606 . In one implementation, the signal bearing medium  2602  may include a recordable medium  2608 . In one implementation, the signal bearing medium  2602  may include a communications medium  2610 .  
       FIG. 27  illustrates an example system  2700  in which embodiments may be implemented. The system  2700  includes a computing system environment. The system  2700  also illustrates the clinician  104  using a device  2704 , which is optionally shown as being in communication with a computing device  2702  by way of an optional coupling  2706 . The optional coupling  2706  may represent a local, wide-area, or peer-to-peer network, or may represent a bus that is internal to a computing device (e.g., in example embodiments in which the computing device  2702  is contained in whole or in part within the device  2704 ). A storage medium  2708  may be any computer storage media.  
      The computing device  2702  includes computer-executable instructions  2710  that when executed on the computing device  2702  cause the computing device  2702  to access at least one dataset, based on at least one treatment parameter and at least one predictive basis, determine data associated with a first possible outcome of a use of the at least one treatment parameter with respect to at least one body portion, based on the at least one dataset, apply a filter criteria to the at least one dataset to obtain a filtered dataset, and determine data associated with a second possible outcome of the use of the at least one treatment parameter with respect to the at least one body portion, based on the filtered dataset.  
      In  FIG. 27 , then, the system  2700  includes at least one computing device (e.g.,  2702  and/or  2704 ). The computer-executable instructions  2710  may be executed on one or more of the at least one computing device. For example, the computing device  2702  may implement the computer-executable instructions  2710  and output a result to (and/or receive data from) the computing (clinician) device  2704 . Since the computing device  2702  may be wholly or partially contained within the computing (clinician) device  2704 , the computing (clinician) device  2704  also may be said to execute some or all of the computer-executable instructions  2710 , in order to be caused to perform or implement, for example, various ones of the techniques described herein, or other techniques.  
      The clinician device  2704  may include, for example, one or more of a personal digital assistant (PDA), a laptop computer, a tablet personal computer, a networked computer, a computing system comprised of a cluster of processors, a workstation computer, and/or a desktop computer. In another example embodiment, the clinician device  2704  may be operable to communicate with the computing device  2702  to communicate with a database (e.g., implemented using the storage medium  2708 ) to access the at least one dataset and/or to access the second dataset.  
      In addition to references described above, the following are also hereby incorporated by reference in their entireties to the extent such are not inconsistent herewith: 
      Pasqualini et al., “Probing the Structural and Molecular Diversity of Tumor Vasculature,” TRENDS in Molecular Medicine, vol. 8, No. 12, pp. 563-571 (December 2002);     Aird et al., “Vascular Bed-specific Expression of an Endothelial Cell Gene is Programmed by the Tissue Microenvironment,” The Journal of Cell Biology, vol. 138, No. 5, pp. 1117-1124 (Sep. 8, 1997);     Pasqualini et al., “Organ Targeting In Vivo Using Phage Display Peptide Libraries,” Nature, vol. 380, pp. 364-366 (Mar. 28, 1996);     Rajotte et al., “Molecular Heterogeneity of the Vascular Endothelium Revealed by In Vivo Phage Display,” J. Clin. Invest., vol. 102, No. 2, pp. 430-437 (July 1998);     M&#39;Rini, et al., “A Novel Endothelial L-Selectin Ligand Activity in Lymph Node Medulla That Is Regulated by (1,3)-Fucosyltransferase-IV,” J. Exp. Med., vol. 198, No. 9, pp. 1301-1312 (Nov. 3, 2003);     Carver, et al., “Caveolae: Mining Little Caves for New Cancer Targets,” Nature Reviews, vol. 3, pp. 571-572 (August 2003);     Folkman, Judah, “Looking For A Good Endothelial Address,” Cancer Cell, pp. 113-115 (March 2002);     Brody, Lawrence C., “Treating Cancer by Targeting a Weakness,” N Engl J Med, 353; 9 pp. 949-950 (1 Sep. 2005);     Farmer, et al., “Targeting the DNA Repair Defect in BRCA Mutant Cells as a Therapeutic Strategy,” Nature, vol. 434, pp. 917-921 (14 Apr. 2005);     Bryant, et al., “Specific Killing of BRCA2-Deficient Tumours with Inhibitors of poly(ADP-ribose) Polymerase,” Nature, vol. 434, pp. 913-917 (14 Apr. 2005);     Hsu, et al., “Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making,” Science, vol. 310, pp. 1680-1683 (9 Dec. 2005);     Kaplan, et al., “VEGFR1-Postive Haematopoietic Bone Marrow Progenitors Initiate The Pre-Metastatic Niche,” Nature, vol. 438, pp. 820-825 (8 Dec. 2005).    

      Those having skill in the art will recognize that the state of the art has progressed to the point where there is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software can become significant) a design choice representing cost vs. efficiency tradeoffs. Those having skill in the art will appreciate that there are various vehicles by which processes and/or systems and/or other technologies described herein can be effected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; alternatively, if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware. Hence, there are several possible vehicles by which the processes and/or devices and/or other technologies described herein may be effected, none of which is inherently superior to the other in that any vehicle to be utilized is a choice dependent upon the context in which the vehicle will be deployed and the specific concerns (e.g., speed, flexibility, or predictability) of the implementer, any of which may vary. Those skilled in the art will recognize that optical aspects of implementations will typically employ optically-oriented hardware, software, and or firmware.  
      The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).  
      In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof can be viewed as being composed of various types of “electrical circuitry.” Consequently, as used herein “electrical circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated Circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.  
      Those skilled in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.  
      The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermediate components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality. Any two components capable of being so associated can also be viewed as being “operably couplable” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.  
      While certain features of the described implementations have been illustrated as disclosed herein, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the embodiments of the invention.  
      While particular aspects of the present subject matter described herein have been shown and described, it will be apparent to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from this subject matter described herein and its broader aspects and, therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of this subject matter described herein. Furthermore, it is to be understood that the invention is solely defined by the appended claims. It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”