Patent Publication Number: US-9424460-B2

Title: Tumor plus adjacent benign signature (TABS) for quantitative histomorphometry

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application 61/950,357 filed Mar. 10, 2014. 
    
    
     BACKGROUND 
     Tens of thousands of prostate cancer (CaP) patients undergo radical prostatectomies (RP) in the United States each year. Despite the effectiveness of RP in treating CaP, 15% to 40% of men will experience, following RP, disease progression manifested as biochemical recurrence, local or distant cancer recurrence, or cancer death. The Gleason sum (GS) is a measure used by pathologists to assess tissue morphology. In Gleason scoring, the two most common Gleason patterns are scored on a scale of 1 to 5. The sum of the two scores is the Gleason Sum, which ranges from 2 through 10 and is the conventional method of predicting CaP progression. High GS cases are correlated with cancer progression. Patients with high GS may be provided with more aggressive secondary treatments in addition to RP. GS is, however, only associated with cancerous foci. Conventional methods of cancer grading that use only GS are unable to grade patterns in benign stromal areas proximal to cancer foci. 
     Pathologists have conventionally used microscopes to conduct visual evaluation of histological tissue. Manual evaluation of histological tissue is excessively time consuming in a clinical environment, and may suffer from poor inter-interpreter agreement. Digital whole slide scanners have enabled automated evaluation of histological tissue through quantitative histomorphometry (QH). Conventional QH methods have used nuclear shape as a predictor of CaP. Other conventional QH methods have used nuclear roundness variance to evaluate the tumor area of prostate tissue for CaP progression with greater effectiveness than traditional Gleason scoring. 
     Some conventional methods have demonstrated the field effect through higher nuclear morphometric scores associated with benign prostate nuclei found near tumor regions. However, conventional methods that investigate benign prostate nuclei use Feulgen-staining of DNA, which is not a standard staining technique employed by pathologists investigating CaP. Thus, conventional methods for predicting CaP progression that rely on just GS use only the tumor tissue to gather information about morphological features. Conventional methods that attempt to gather information from benign regions use non-standard staining techniques that pathologists may not be trained to analyze. The use of non-standard staining techniques may increase the time required to implement those methods, and reduce the accuracy of those methods. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate various example apparatus, methods, and other example embodiments of various aspects of the invention. It will be appreciated that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. One of ordinary skill in the art will appreciate that in some examples one element may be designed as multiple elements or that multiple elements may be designed as one element. In some examples, an element shown as an internal component of another element may be implemented as an external component and vice versa. Furthermore, elements may not be drawn to scale. 
         FIG. 1  illustrates an example method of predicting CaP progression in a patient using field effects in automated QH. 
         FIG. 2  illustrates an iteration of a method associated with predicting CaP progression in a patient using field effects in automated QH. 
         FIG. 3  illustrates an example method of identifying disease progression in a cancer patient. 
         FIG. 4  illustrates an example apparatus that predicts cancer progression in a patient. 
         FIG. 5  illustrates an example computer in which example methods and apparatus described herein operate. 
     
    
    
     DETAILED DESCRIPTION 
     Nearly 75,000 radical prostatectomies are performed each year in the United States to treat CaP. After undergoing RP, up to 40% of patients will experience disease progression manifested as biochemical recurrence, local or distant cancer recurrence, or death. Improving the accuracy with which CaP progression may be predicted has the concrete and tangible result of increasing treatments and resources directed to those patients more likely to suffer CaP progression, and reducing un-needed treatments and resources directed towards those patients unlikely to suffer CaP progression. 
     Conventional methods for predicting CaP progression typically employ Gleason scoring. The Gleason Sum (GS) is a measure used by pathologists to assess tissue morphology. High GS score cases are correlated with cancer progression. Patients with high GS scores may be provided with more aggressive secondary treatment in conjunction with RP. Patients with lower GS scores may be provided with less aggressive treatments. Conventional methods that employ the GS traditionally involve a human pathologist using a microscope to make a visual evaluation of a section of histological tissue. Digital whole slide scanners have allowed the development of QH for automated evaluation of histological tissue to complement manual pathologist evaluation. QH allows automated evaluation of nuclear shape in cancerous tissue as a predictor for CaP. QH also enables automated evaluation of nuclear roundness variance among cancerous nuclei in the tumor area, which, in some cases, results in better accuracy than Gleason scoring when predicting CaP progression. However, conventional methods that employ QH and GS do not leverage the field effect associated with benign prostate nuclei found near tumor regions. 
     The field effect describes the micro-environment around the site of a tumor that may lead to a progression of the disease at another site. Predicting CaP progression using the field effect involves analyzing the benign stromal tissue found adjacent to cancerous tissue. Higher nuclear morphometric scores for benign prostate nuclei near tumor regions indicate that there are visual cues that may serve as markers for disease progression from within the benign regions. Conventional methods for analyzing the field effect in benign regions use Feulgen-staining of DNA rather than standard H&amp;E stained slides. Pathologists are more likely to be familiar with H&amp;E stained slide analysis than with Feulgen DNA staining. Thus, conventional methods of analyzing the field effect are not clinically optimal, since a pathologist would have to undergo costly and time-consuming training to learn how to implement Feulgen-stained DNA analysis. 
     Example methods and apparatus predict CaP progression using cell graph features extracted from H&amp;E stained slides of benign regions along with nuclear morphological descriptors obtained from cancer regions. Example methods and apparatus construct a cell graph of a benign region surrounding or adjacent to a tumor area. Example methods and apparatus extract a set of graph features from the graph of the benign region. Example methods and apparatus extract a set of morphological features from tumor cells. Example methods and apparatus select the features from each set that are most prognostically informative for predicting CaP progression. The top selected features are combined into a tumor plus adjacent benign signature (TABS) set of features. The TABS set of features may be employed with Gleason scoring to identify with greater accuracy than conventional methods CaP patients who will experience disease progression following RP. For example, while conventional features obtained from cancerous tissue sections show a predictive area under the curve (AUC) of at best 0.72, example methods and apparatus add features obtained from the benign regions and increase the AUC to 0.74. When example methods and apparatus employ TABS along with GS, the AUC increases to at least 0.82. By increasing the accuracy with which CaP progression is predicted, example methods and apparatus produce the concrete, real-world result of increasing the probability that at-risk patients receive timely treatment, and reducing the expenditure of resources and time on patients who are less likely to demonstrate CaP progression. Example methods and apparatus thus improve on conventional methods in a measurable, clinically significant way. 
     Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a memory. These algorithmic descriptions and representations are used by those skilled in the art to convey the substance of their work to others. An algorithm, here and generally, is conceived to be a sequence of operations that produce a result. The operations may include physical manipulations of physical quantities. Usually, though not necessarily, the physical quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a logic, and so on. The physical manipulations create a concrete, tangible, useful, real-world result. 
     It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, and so on. It should be borne in mind, however, that these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, it is appreciated that throughout the description, terms including processing, computing, calculating, determining, and so on, refer to actions and processes of a computer system, logic, processor, or similar electronic device that manipulates and transforms data represented as physical (electronic) quantities. 
     Example methods may be better appreciated with reference to flow diagrams. While for purposes of simplicity of explanation, the illustrated methodologies are shown and described as a series of blocks, it is to be appreciated that the methodologies are not limited by the order of the blocks, as some blocks can occur in different orders and/or concurrently with other blocks from that shown and described. Moreover, less than all the illustrated blocks may be required to implement an example methodology. Blocks may be combined or separated into multiple components. Furthermore, additional and/or alternative methodologies can employ additional, not illustrated blocks. 
       FIG. 1  illustrates a computerized method  100  of predicting CaP progression in a patient using field effects in automated quantitative histomorphometry (QH). Method  100  includes, at  110 , accessing a first digital image of a cancerous section of a prostate that demonstrates pathology associated with CaP in the patient. In one embodiment, the first digital image is of a section of tumor tissue. Accessing an image includes acquiring electronic data, reading from a computer file, receiving a computer file, reading from a computer memory, or other computerized activity. In one embodiment, the first digital image may be a tissue microarray (TMA) image. More generally, the digital image is a digital image of a tissue slide. In one embodiment, the image is a 1670 pixel by 1670 pixel image of a 0.6 mm tissue microarray core stained with H&amp;E. In one embodiment, the image may be acquired by scanning the image from a high power field (HPF) of an H&amp;E stained tissue slide. In this embodiment, the image is acquired using a slide scanner and a multi-spectral microscope. The slide-scanner may be, for example, an Aperio XT scanner. In other embodiments, the image may have different dimensions and may be acquired from other systems. In still other embodiments, the image may represent a different sized tissue microarray core, or the microarray core may be stained with a different technique. 
       FIG. 1  also includes, at  120 , detecting a first cell in a first region of interest of the first digital image. In one embodiment, the first cell is a tumor cell detected in a tumor region of the first digital image. The tumor region of the first digital image is distinguishable from a benign region of the first digital image. The first cell is detected using a shape-based active contour segmentation scheme. 
       FIG. 1  also includes, at  130 , segmenting the boundary of the first cell into a set of cell boundary points. The first cell boundary is segmented using the shape-based active contour segmentation scheme. The segmentation scheme includes an energy functional of the active contour. 
     In one embodiment, the energy functional has three terms. The energy functional may be defined as F=β s +∫ Ω (φ(x)−ψ(x)) 2 |∇φ|δ(φ)dx+β r ∫ Ω θ in H ψ dx+∫ Ω θ out H −ψ dx. In this embodiment, the first term β s ∫ Ω (φ(x)−ψ(x)−ψ(x)) 2 |∇φ|δ(φ)dx represents a shape plus boundary force. The shape plus boundary force is a prior shape term modeled on a prostate cell nucleus. The shape plus boundary force constrains the deformation achievable by the active contour. In another embodiment, the prior shape term may be modeled on a cell nucleus other than a prostate cell nucleus. 
     The energy function has a second term and a third term. The second and third terms represent a region force. The second term, β r ∫ Ω θ in H ψ dx, is a boundary-based term that detects nuclear boundaries from image gradients. The third term, ∫ Ω θ out H −ψ dx, drives the shape prior and the contour towards the nuclear boundary based on region statistics. 
     In this embodiment, β s ,β r &gt;0 are constants that balance contributions of the shape plus boundary force and the region force. {φ} is a level set function. ψ is a shape prior. δ(φ) is a contour measure on {φ=0}. H(.) is the Heaviside function. θ r =I/−u r | 2 +μ|∇u r | 2 , where rε{in, out}. u r  is defined such that rε{in, out} are partitioned foreground and background regions. Ω is a bounded open set in    2 . 
     Method  100  also includes, at  140 , extracting a set of morphological features from the set of cell boundary points. In one embodiment, method  100  extracts a set of at least  100  morphological features based on the segmentation of the boundary of the first cell into a set of cell boundary points. Extracting the set of morphological features from the set of cell boundary points includes calculating a set of morphological features based, at least in part, on statistics related to the set of cell boundary points. These statistics include nuclear area and smoothness. In another embodiment, a different number of morphological features may be extracted based on statistics other than nuclear area and smoothness. 
     Method  100  also includes, at  150 , accessing a second digital image of a benign section of the prostate. In one embodiment, the second digital image of the benign section of the prostate is an image of a section of a benign region of the prostate located adjacent to the tumor region of the prostate. The benign region is distinguishable from the tumor region. In another embodiment, the second digital image of the benign section of the prostate is acquired from the same section of tissue as the digital image of the tumor region of the prostate. In one embodiment, the second digital image may be a TMA image. 
     Method  100  also includes, at  160 , detecting a second cell in a second region of interest in the second digital image. In one embodiment, the second cell is detected using the shape-based active contour segmentation scheme. In another embodiment, the cell may be detected using a different segmentation scheme. 
     Method  100  also includes, at  170 , constructing a subgraph of a localized cell network within the second region of interest. Example apparatus and methods extract features from benign tissue regions adjacent to tumor regions using localized subgraphs. Nodes of the subgraph represent individual cell nuclei centroids. Edges of the subgraph are defined between pairs of nodes by a probabilistic decaying function. A subgraph G={V, E} is defined where V represents the set of n nuclear centroids γ i ,γ j εV. Nodes are defined as i,jε{1, 2, . . . , n}. E represents the set of edges that connect the nodes in the subgraph G. The edges between the pairs of nodes γ i ,γ i  are set as a probabilistic decaying function
 
 E ={( i,j ): r&lt;d ( i,j ) −α , ∀γ i ,γ j   εV} 
 
where d(i,j) represents the Euclidean distance between nodes γ i  and γ j . The density of the graph is controlled by α&gt;0. Values of α approaching 0 indicate a high probability of connecting nodes. Values of a approaching infinity indicate a low probability of connecting nodes. rε[0, 1] is an empirically determined edge threshold. In one embodiment, rε[0, 1] may be generated by a random number generator.
 
     Method  100  also includes, at  180 , extracting a set of subgraph features from the subgraph. The set of subgraph features characterizes a field effect of the local cell organization. For example, a benign non-progressor may display a sparser arrangement of cells visible in a graph than a benign core of a progressor. Subgraphs that capture the different arrangements between progressors and non-progressors enable automated QH to predict disease progression based on the properties of the benign tissue. By employing H&amp;E stained slides, method  100  improves on conventional methods that employ Feulgen-staining of DNA. 
     In one embodiment, the set of subgraph features includes at least 26 features. The set of subgraph features includes eccentricity and connected component size. In another embodiment, other features, and other numbers of features, may be extracted. 
     Method  100  also includes, at  190 , producing a set of TABS features from the set of morphological features and the set of subgraph features. In one embodiment, producing the set of TABS features includes selecting at least a threshold number of the most prognostically informative features for predicting CaP progression from the set of morphological features and from the set of subgraph features. For example, 7 nuclear subgraph features, 1 nuclear morphology feature, and 1 nuclear density feature may be the most prognostically informative features extracted from the benign region. Similarly, 9 nuclear morphology features and 1 Delaunay triangulation may be the most prognostically informative features extracted from the tumor region. In this example, the combined TABS feature set may include 7 nuclear subgraph features from the benign region, 1 nuclear morphology feature from the tumor region, 1 Delaunay triangulation from the tumor region, and 1 nuclear morphology feature from the benign region. In one embodiment, the threshold number is 10, and the threshold number of features are selected using a Wilcoxon Rank Sum test. In other embodiments, other numbers of features may be selected, and other tests, including other non-parametric tests of the null hypothesis, may be used to select the threshold number of features. 
     Method  100  also includes, at  194 , controlling a computer to predict CaP progression in the patient, based, at least in part, on the set of TABS features. In one embodiment, a computer aided diagnostic system (CADx) is controlled by method  100  to calculate a probability that the digital image under analysis represents a progressor or a non-progressor. The CADx calculated probability may then be employed to complement a human pathologist&#39;s determination that the digital image represents a progressor or a non-progressor. 
     Improved prediction of CaP progression using automated QH may produce the technical effect of improving treatment efficacy and improving doctor efficiency by increasing the accuracy and decreasing the time required to predict CaP progression. Treatments and resources may be more accurately tailored to patients with more aggressive cancer so that more appropriate protocols may be employed. Using a more appropriate protocol may lead to less therapeutics being required for a patient or may lead to avoiding or delaying a resection or other invasive procedure. When CaP progression is more quickly and more accurately detected, patients most at risk may receive a higher proportion of scarce resources (e.g., therapeutics, physician time and attention, hospital beds) while those less at risk may be spared unnecessary treatment, which in turn spares unnecessary expenditures and resource consumption. Example methods and apparatus may also have the concrete effect of improving patient outcomes. 
     While  FIG. 1  illustrates various actions occurring in serial, it is to be appreciated that various actions illustrated in  FIG. 1  could occur substantially in parallel. By way of illustration, a first process could detect and segment cellular boundaries in the first digital image, a second process could construct a subgraph, and a third process could construct a set of TABS features. While three processes are described, it is to be appreciated that a greater or lesser number of processes could be employed and that lightweight processes, regular processes, threads, and other approaches could be employed. 
       FIG. 2  illustrates an iteration of a method  200  associated with predicting CaP progression in a patient using field effects in automated QH. Method  200  is similar to method  100 , but the embodiment illustrated in  FIG. 2  shows method  200  operating in parallel, instead of serially, as method  100  is illustrated in  FIG. 1 . Method  200  includes, at  210 , accessing an image of a tumor region of a section of prostate tissue. Method  200 , at  220 , detects a tumor cell in the image. At  230 , method  200  segments the cell boundary of the tumor cell into a set of cell boundary points. In one embodiment, method  200  uses a shape-based active contour segmentation scheme to detect and segment the tumor cell. Method  200  also includes, at  240 , extracting a set of morphological features from the set of cell boundary points. Method  200  also includes, at  250 , selecting the top ten morphological features from the set of morphological features. The top ten morphological features are the ten most prognostically informative morphological features. Method  200  may select the top ten most prognostically informative features from the set of morphological features using a Wilcoxon Rank Sum test. Method  200  may also calculate Delaunay triangulation features or Voronoi polygon area features from the tumor image and these features may be included in the top ten morphological features. In another embodiment, more or less than ten features may be selected. 
     Method  200  also includes, at  215 , accessing an image of a benign region of the prostate. Method  200 , at  225 , detects a benign cell in the image. In one embodiment, method  200  uses a shape-based active contour segmentation scheme to detect the benign cell. Method  200 , at  235 , constructs a subgraph of a localized cellular network in the benign region of the prostate represented in the benign image. The nodes of the subgraph represent individual cell nuclei centroids. The edges of the subgraph are defined by a probabilistic decaying function of the Euclidean distance between a pair of nodes. The density of the subgraph is user-adjustable. Method  200 , at  245 , extracts a set of subgraph features from the subgraph. The set of subgraph features may include statistics of Voronoi polygon area, Delaunay edge length, or nuclear density features that describe the clustering of nuclei. In another embodiment, other features may be extracted from the subgraph. Method  200  also includes, at  255 , selecting the top ten subgraph features from the set of subgraph features. The top ten subgraph features are the ten subgraph features that are most prognostically informative for predicting CaP progression. The top ten subgraph features are selected, in one embodiment, using a Wilcoxon Rank Sum test. In another embodiment the top ten subgraph features may be selected using a different technique. In another embodiment, more or less than ten subgraph features may be selected. 
     In method  200 , steps  210 ,  220 ,  230 ,  240 , and  250  (tumor steps) occur in parallel with steps  215 ,  225 ,  235 ,  245 , and  255  (graph steps). In one embodiment, the image associated with the tumor and the image associated with the benign area are separate images of tissue acquired from the same prostate. Since the image associated with the tumor and the image associated with the benign area are separate images, analysis of the images does not need to be conducted serially. Performing the tumor steps and benign steps in parallel may reduce the amount of time needed produce the set of TABS features. Faster prediction of CaP progression may enable application of treatments to CaP patients suffering from aggressive forms of CaP in a more clinically relevant time-frame than conventional methods. 
     Method  200  also includes, at  260 , producing a set of TABS features. The set of TABS features includes the top ten most prognostically informative features from both the set of morphological features and the set of subgraph features. Method  200 , at  260 , selects the top ten most prognostically informative features from both the set of morphological features and the set of subgraph features. For example, method  200  may select three morphological features and seven subgraph features. In one embodiment, method  200  selects the TABS features using a Wilcoxon Rank Sum test. In another embodiment, the TABS features may be selected using a different test. In still another embodiment, the set of TABS features may include more or less than ten features. 
     Method  200  also includes, at  270 , predicting CaP progression based, at least in part, on the set of TABS features. In one embodiment, predicting CaP progression includes calculating the probability that a tumor is a progressor or a non-progressor based, at least in part, on the set of TABS features and a GS for the tumor. Method  200  may control a CADx system to predict CaP progression based, at least in part, on the set of TABS features. 
       FIG. 3  illustrates an example method  300  of identifying disease progression in a cancer patient. Method  300  includes, at  310 , accessing an image of a section of tissue. The section of tissue includes a cancerous region and a benign region. The cancerous region is distinguishable from the benign region. In one embodiment, the image is a TMA image of a section of prostate exhibiting CaP. In another embodiment, the image may be an image of a section of breast cancer tissue, an image of a section of lung cancer tissue, or an image of a section of tissue exhibiting a type of cancer where the field effect describes a micro-environment around the site of a tumor that leads to a progression of the disease at another site. 
     Method  300  also includes, at  310 , detecting a tumor cell in the cancerous region of the image. In one embodiment, method  300  uses a shape-based active contour segmentation scheme to detect the tumor cell. The shape-based active contour segmentation scheme includes an energy functional of the active contour. The energy functional may be a three-term functional. The energy functional may include a prior shape term modelled on tumor cell nuclei, a boundary-based term that detects the nuclear boundaries from image gradients, and a driving term that drives the shape prior and the contour towards the nuclear boundary based on region statistics. In another embodiment, a different scheme may be employed to detect the tumor cell. 
     Method  300  also includes, at  320 , detecting a benign cell in the benign region of the image. In one embodiment, method  300  uses a shape-based active contour segmentation scheme to detect the benign cell. The shape-based active contour segmentation scheme includes an energy functional of the active contour. The energy function may be a three-term functional including a prior shape term modelled on benign cell nuclei, a boundary-based term that detects the nuclear boundaries from image gradients, and a driving term that drives the shape prior and the contour towards the nuclear boundary based on region statistics. In another embodiment, a different scheme may be used to detect the benign cell. In one embodiment, the shape-based active contour segmentation scheme used to detect a benign cell is the same shape-based active contour segmentation scheme used to detect a tumor cell. In another embodiment, different shape-based active contour segmentation schemes may be used. 
     Method  300  also includes, at  340 , segmenting the boundary of the tumor cell into a set of boundary points using the shape-based active contour segmentation scheme. 
     Method  300  also includes, at  350 , extracting a set of morphological features from the set of boundary points. The set of morphological features may be extracted from the set of boundary points segmented from the tumor cell. The set of morphological features are calculated from a set of statistics. In one embodiment, the set of statistics includes nuclear area and smoothness. 
     Method  300  also includes, at  360 , constructing a subgraph of a localized cell network in the benign region. In one embodiment, the nodes of the subgraph represent individual cell nuclei centroids. The edges of the subgraph are defined between pairs of nodes by a probabilistic decaying function of the Euclidean distance between a first node and a second node. In one embodiment, the density of the subgraph is controllable by a user. 
     Method  300  also includes, at  370 , extracting a set of subgraph features from the subgraph. The set of subgraph features describe the spatial organization of cell nuclei within the tissue represented in the benign image. The spatial organization of cell nuclei described by the set of subgraph features enables method  300  to use field effects detected in the benign region to predict cancer progression. In one embodiment, the set of subgraph features includes architectural features including Voronoi diagrams, Delaunay triangulations, Voronoi polygon area, Delaunay edge length, and nuclear density features. In another embodiment, a subgraph of the tumor region may also be constructed, and subgraph features from the subgraph of the tumor region may be extracted. 
     Method  300  also includes, at  380 , producing a set of signature features. The set of signature features includes a subset of the set of morphological features and a subset of the set of subgraph features. In one embodiment, method  300  uses a Wilcoxon Rank Sum test to select the most prognostically informative subset of features from the set of morphological features and the set of subgraph features. In one embodiment, the set of signature features includes at least two features selected from the set of morphological features. 
     Method  300  also includes, at  390 , classifying the section of tissue. Classifying the section of tissue includes controlling a computer aided diagnostic (CADx) system to classify the section of tissue as a progressor or a non-progressor. The classification is based, at least in part, on the set of signature features. In one embodiment, method  300  controls the CADx system to classify the section of tissue based on the set of signature features and a GS for the section of tissue. Classifying the section of tissue using both the set of signature features and the GS provides improved accuracy compared to conventional methods of predicting cancer progression that employ either Gleason scoring or a set of graph features, but not both. 
     Example methods and apparatus improve the prediction of cancer progression compared to conventional methods by using the TABS features instead of just features extracted from the cancerous region. The combined tumor and adjacent benign features included in the TABS feature set increase the accuracy of predicting cancer progression using automated QH compared to conventional methods. Employing the TABS feature set in combination with GS further improves on conventional methods. Example methods and apparatus leverage disconnected feature sets that are not exploited by conventional methods, which facilitates making more accurate predictions of patient prognosis. Improving patient prognosis prediction facilitates allocating resources, personnel, and therapeutics to appropriate patients while sparing other patients from treatment that might have been prescribed with a less accurate prediction. 
     In one example, a method may be implemented as computer executable instructions. Thus, in one example, a computer-readable storage medium may store computer executable instructions that if executed by a machine (e.g., computer) cause the machine to perform methods described or claimed herein including method  100 , method  200 , and method  300 . While executable instructions associated with the listed methods are described as being stored on a computer-readable storage medium, it is to be appreciated that executable instructions associated with other example methods described or claimed herein may also be stored on a computer-readable storage medium. In different embodiments the example methods described herein may be triggered in different ways. In one embodiment, a method may be triggered manually by a user. In another example, a method may be triggered automatically. 
       FIG. 4  illustrates an example apparatus  400  that predicts cancer progression in a patient. Apparatus  400  includes a processor  410 , a memory  420 , an input/output interface  430 , a set of logics  440 , and an interface  450  that connects the processor  410 , the memory  420 , the input/output interface  430 , and the set of logics  440 . The set of logics  440  includes an image acquisition logic  441 , a detection logic  443 , a morphology logic  445 , a graph logic  447 , a tumor plus adjacent benign signature (TABS) logic  448 , and a prediction logic  449   
     Image acquisition logic  441  acquires an image of a region of tissue. The region of tissue may be a section of tissue demonstrating cancerous pathology in a patient. The image may be a 1670 pixel by 1670 pixel image of a 0.6 mm TMA core stained with H&amp;E. The TMA core may be sampled from a cancerous region or from a benign region adjacent to the cancerous region. More generally, the image may be a digital image of a tissue slide. In one embodiment, image acquisition logic  441  acquires a digitally scanned H&amp;E stained image from a digital stain scanner. In another embodiment, images that are made using other scanners, other staining techniques, other dimensions, or different magnification levels may be acquired. For example, the image may be provided by an optical microscope or an automated slide staining system. Thus, accessing the image may include interacting with a scanning apparatus, an optical microscope, or an automated slide staining system. Other imaging systems may be used to generate and access the image accessed by image acquisition logic  441 . 
     Detection logic  443  detects and segments a cell boundary as a function of a shape-based active contour segmentation scheme. In one embodiment, the shape-based active contour segmentation scheme includes a three-term energy functional. The energy functional includes a shape prior term. The shape prior term constrains the deformation achievable by the active contour. The energy functional also includes a boundary-based term that detects cellular boundaries from image gradients. The energy functional also contains a driving term. The driving term drives the shape prior and the contour towards the cell boundary based, at least in part, on region statistics. In another embodiment, the boundary-based term detects nuclear boundaries or cellular boundaries, and the driving term drives the shape prior and contour towards the nuclear boundary or the cell boundary based, at least in part, on region statistics. In another embodiment, detection logic  443  detects and segments a cell boundary using a different segmentation scheme. The different segmentation scheme may have a different number of terms. 
     Morphology logic  445  extracts a set of morphological features from the set of cell boundary points. In one embodiment, morphology logic  445  extracts  173  features from the cell of cell boundary points. In one embodiment, morphology logic  445  selects at least a threshold number of the most prognostically significant features from the set of morphology features. In one embodiment, the threshold number is ten. In another embodiment, the threshold number may be more or less than ten. In one embodiment, morphology logic  445  selects the threshold number of features from the set of morphological features as a function of a Wilcoxon Rank Sum test. In another embodiment, morphology logic  445  may use a different test to select the most prognostically significant features. 
     Graph logic  447  constructs a graph of a localized cellular neighborhood detected in the benign region. Nodes in the graph represent nuclei in the region of tissue. The probability that a first node in the graph is connected to a second, different node in the graph is based on a probabilistic decaying function of the Euclidean distance between the first node and the second node. In one embodiment, the density of the graph is a function of the Euclidean distance between nodes and a user-controllable parameter. Graph logic  447  also extracts a set of graph features from the graph. The graph features include Voronoi polygon area, Delaunay edge length, or nuclear density features. In one embodiment, graph logic  447  extracts at least N graph features from the graph. In this embodiment, N is a number equal to or greater than 26. In another embodiment, N may have different values. For example, graph logic  447  may extract  173  features from the graph. 
     TABS logic  448  constructs a set of TABS features. The set of TABS features includes a subset of the set of morphological features and a subset of the set of subgraph features. In one embodiment, TABS logic  448  uses a Wilcoxon Rank Sum test to select at least a threshold number of the most prognostically significant features from the set of morphological features and the set of graph features. In one embodiment, the threshold number is at least ten. TABS logic  448  selects at least X graph features and at least Y morphological features to construct the set of TABS features. X and Y are numbers. In one embodiment, X is at least seven and Y is at least two. 
     Prediction logic  449  calculates the probability that a tumor represented in the image is a progressor or a non-progressor. Prediction logic  449  calculates the probability based, at least in part, on the set of TABS features. In one embodiment, prediction logic  449  calculates the probability based on both the set of TABS features and a Gleason scoring of the image. Calculating the probability based on both the set of TABS features and a Gleason scoring of the image results in apparatus  400  achieving an area under the curve value of at least 0.82 with a p-value of 0.0015. Apparatus  400  thus improves on conventional cancer prediction apparatuses which employ Gleason scoring or morphological features separately. 
     In another embodiment, prediction logic  449  may control a computer aided diagnosis (CADx) system to classify the image based, at least in part, on the probability that the tumor is a progressor or a non-progressor. For example, prediction logic  449  may control a computer aided CaP diagnostic system to grade the image based, at least in part, on the set of TABS features. In other embodiments, other types of CADx systems may be controlled, including CADx systems for grading colon cancer, lung cancer, bone metastases, breast cancer, and other diseases where disease progression may be predicted based on a set of TABS features. Prediction logic  449  may control the CADx system to display the prediction on a computer monitor, a smartphone display, a tablet display, or other displays. Displaying the prediction may also include printing the prediction. Prediction logic  449  may also control the CADx to display an image of the tumor region and an image of the benign region. The image of the tumor region may include the set of morphological features. The image of the benign region may include a visual representation of a graph of the localized cellular neighborhood in the benign region. 
       FIG. 5  illustrates an example computer  500  in which example methods illustrated herein can operate and in which example logics may be implemented. In different examples computer  500  may be part of a digital whole slide scanner, may be operably connectable to a digital whole slide scanner, may be part of a microscope, may be operably connected to a microscope, or may be part of a CADx system. 
     Computer  500  includes a processor  502 , a memory  504 , and input/output ports  510  operably connected by a bus  508 . In one example, computer  500  may include a set of logics  530  that perform a method of predicting CaP progression in a cancer patient using field effects in automated QH. Thus, the set of logics  530 , whether implemented in computer  500  as hardware, firmware, software, and/or a combination thereof may provide means (e.g., hardware, software) for predicting CaP progression in a cancer patient using field effects in automated QH. In different examples, the set of logics  530  may be permanently and/or removably attached to computer  500 . 
     Processor  502  can be a variety of various processors including dual microprocessor and other multi-processor architectures. Memory  504  can include volatile memory and/or non-volatile memory. A disk  506  may be operably connected to computer  500  via, for example, an input/output interface (e.g., card, device)  518  and an input/output port  510 . Disk  506  may include, but is not limited to, devices like a magnetic disk drive, a tape drive, a Zip drive, a flash memory card, or a memory stick. Furthermore, disk  506  may include optical drives like a CD-ROM or a digital video ROM drive (DVD ROM). Memory  504  can store processes  514  or data  517 , for example. Disk  506  or memory  504  can store an operating system that controls and allocates resources of computer  500 . 
     Bus  508  can be a single internal bus interconnect architecture or other bus or mesh architectures. While a single bus is illustrated, it is to be appreciated that computer  500  may communicate with various devices, logics, and peripherals using other busses that are not illustrated (e.g., PCIE, SATA, Infiniband, 1394, USB, Ethernet). 
     Computer  500  may interact with input/output devices via I/O interfaces  518  and input/output ports  510 . Input/output devices can include, but are not limited to, digital whole slide scanners, an optical microscope, a keyboard, a microphone, a pointing and selection device, cameras, video cards, displays, disk  506 , network devices  520 , or other devices. Input/output ports  510  can include but are not limited to, serial ports, parallel ports, or USB ports. 
     Computer  500  may operate in a network environment and thus may be connected to network devices  520  via I/O interfaces  518  or I/O ports  510 . Through the network devices  520 , computer  500  may interact with a network. Through the network, computer  500  may be logically connected to remote computers. The networks with which computer  500  may interact include, but are not limited to, a local area network (LAN), a wide area network (WAN), or other networks. 
     References to “one embodiment”, “an embodiment”, “one example”, and “an example” indicate that the embodiment(s) or example(s) so described may include a particular feature, structure, characteristic, property, element, or limitation, but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element or limitation. Furthermore, repeated use of the phrase “in one embodiment” does not necessarily refer to the same embodiment, though it may. 
     “Computer-readable storage medium”, as used herein, refers to a medium that stores instructions or data. “Computer-readable storage medium” does not refer to propagated signals. A computer-readable storage medium may take forms, including, but not limited to, non-volatile media, and volatile media. Non-volatile media may include, for example, optical disks, magnetic disks, tapes, and other media. Volatile media may include, for example, semiconductor memories, dynamic memory, and other media. Common forms of a computer-readable storage medium may include, but are not limited to, a floppy disk, a flexible disk, a hard disk, a magnetic tape, other magnetic medium, an application specific integrated circuit (ASIC), a compact disk (CD), other optical medium, a random access memory (RAM), a read only memory (ROM), a memory chip or card, a memory stick, and other media from which a computer, a processor or other electronic device can read. 
     “Logic”, as used herein, includes but is not limited to hardware, firmware, software in execution on a machine, or combinations of each to perform a function(s) or an action(s), or to cause a function or action from another logic, method, or system. Logic may include a software controlled microprocessor, a discrete logic (e.g., ASIC), an analog circuit, a digital circuit, a programmed logic device, a memory device containing instructions, and other physical devices. Logic may include one or more gates, combinations of gates, or other circuit components. Where multiple logical logics are described, it may be possible to incorporate the multiple logical logics into one physical logic. Similarly, where a single logical logic is described, it may be possible to distribute that single logical logic between multiple physical logics. 
     To the extent that the term “includes” or “including” is employed in the detailed description or the claims, it is intended to be inclusive in a manner similar to the term “comprising” as that term is interpreted when employed as a transitional word in a claim. 
     Throughout this specification and the claims that follow, unless the context requires otherwise, the words ‘comprise’ and ‘include’ and variations such as ‘comprising’ and ‘including’ will be understood to be terms of inclusion and not exclusion. For example, when such terms are used to refer to a stated integer or group of integers, such terms do not imply the exclusion of any other integer or group of integers. 
     To the extent that the term “or” is employed in the detailed description or claims (e.g., A or B) it is intended to mean “A or B or both”. When the applicants intend to indicate “only A or B but not both” then the term “only A or B but not both” will be employed. Thus, use of the term “or” herein is the inclusive, and not the exclusive use. See, Bryan A. Garner, A Dictionary of Modern Legal Usage  624  (2d. Ed. 1995). 
     While example systems, methods, and other embodiments have been illustrated by describing examples, and while the examples have been described in considerable detail, it is not the intention of the applicants to restrict or in any way limit the scope of the appended claims to such detail. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the systems, methods, and other embodiments described herein. Therefore, the invention is not limited to the specific details, the representative apparatus, and illustrative examples shown and described. Thus, this application is intended to embrace alterations, modifications, and variations that fall within the scope of the appended claims.