Abstract:
An apparatus and method for automatically generating radiation treatment planning parameters are disclosed. In accordance with the illustrative embodiment, a database is constructed that stores: (i) patient data and past treatment plans by expert human planners for these patients, and (ii) optimal treatment plans that are generated using multi-objective optimization and Pareto front search and that represent the best tradeoff opportunities of the patient case, and a predictive model (e.g., a neural network, a decision tree, a support vector machine [SVM], etc.) is then trained via a learning algorithm on a plurality of input/output mappings derived from the contents of the database. During training, the predictive model is trained to identify and infer patterns in the treatment plan data through a process of generalization. Once trained, the predictive model can then be used to automatically generate radiation treatment planning parameters for new patients.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Patent Application No. 61/365,105, filed Jul. 16, 2011, entitled “Automatic Generation of Patient-Specific IMRT Planning Parameters By Learning From Prior Plans,” which is also incorporated by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to cancer radiation therapy, and, more particularly, to an automated method of generating patient-specific planning parameters for radiation therapy. 
       BACKGROUND OF THE INVENTION 
       [0003]    Radiation therapy, or radiotherapy, is the medical use of ionizing radiation to control malignant cells. In intensity-modulated radiation therapy (IMRT), the intensity or segment of the radiation is modified in accordance with a treatment plan to deliver highly conformal radiation doses to the patient target volume (PTV) of malignant cells, while sparing the surrounding organs at risk (OARs) and other healthy tissues from radiation damage. By dividing the PTV and OAR volumes into individual volume elements (or “voxels”), the IMRT treatment plan can be characterized by a three dimensional dose distribution that characterizes the magnitude of radiation at each of the voxels. An effective two dimensional representation of the dose distribution is the dose volume histogram (DVH). 
         [0004]    The development of an intensity-modulated radiation therapy (IMRT) treatment plan (or simply “IMRT planning”) typically involves a complex optimization procedure by which the radiation beam angles and strengths are designed to achieve required dose of radiation for the patient target volume, as well as limit the radiation delivered to neighboring normal tissues as prescribed. While a portion of the IMRT planning process may be performed via computerized optimization algorithms, typically much of the process requires the input and expertise of a human planner. 
         [0005]    In particular, the human planner is typically responsible for manually adjusting planning dose objectives (e.g., dose limits, dose volume histogram [DVH] limits, etc.) via a time-consuming, iterative trial-and-error process. The trial-and-error nature of the process is due to the fact that the planner does not know whether or not a set of given dose objectives will result in a plan that meets all physician-prescribed goals for sparing organs at risk (known as “sparing goals”), or when it does, whether tradeoffs between patient target volume (PTV) coverage and sparing of organs at risk (OARs) can be further improved. 
         [0006]    Further compounding the process is the fact that physician-prescribed sparing goals are often adapted from clinical trial studies for general populations (e.g., the Radiation Therapy Oncology Group&#39;s (RTOG) sparing goals, the QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic) toxicity data, etc.) that ignore specific anatomical, geometric, and demographic information for individual patients, and often represent the upper limit of an organ&#39;s dose tolerance rather than an individual patient&#39;s lowest achievable dose in that organ. Moreover, because of the lack of quantitative tools for linking variations in anatomy to variations in OAR sparing doses, planners must rely on personal experience and expertise when making adjustments for individual patients. 
         [0007]    What is needed, therefore, is a method of predicting intensity-modulated radiation therapy (IMRT) treatment planning parameters that account for anatomical and other variations between patients, without relying entirely on human planner judgment. 
       SUMMARY OF THE INVENTION 
       [0008]    The present invention comprises an apparatus and method for the automated generation of radiation treatment planning parameters. In accordance with the illustrative embodiment, a database of training treatment plans is constructed that stores patient data and one or both of: 
         [0009]    (i) past treatment plans by expert human planners for these patients, and 
         [0010]    (ii) optimal treatment plans that are generated using multi-objective optimization and Pareto front search, and that represent the best tradeoff opportunities of the patient case, 
         [0000]    and a predictive model (e.g., a neural network, a decision tree, a support vector machine [SVM], a statistical regression, etc.) is then trained via a learning algorithm on a plurality of input/output mappings derived from the contents of the database. 
         [0011]    In accordance with the illustrative embodiment, each input/output mapping corresponds to a particular patient, where the input comprises features that include: 
         [0012]    (i) data based on a geometric (linear or non-linear) characterization of one or more organs at risk proximate to the patient&#39;s target volume, as well as a characterization of the patient&#39;s target volume in relation to one or more of these organs; 
         [0013]    (ii) the size (i.e., volume) and shape of the patient&#39;s target volume, and 
         [0014]    (iii) the size(s) and shape(s) of the organ(s) at risk, which includes partial portions of interest (e.g., an overlap portion with target, a portion outside the primary radiation field, etc.); 
         [0015]    (iv) the sparing characteristics of organ(s) at risk (e.g., serial versus parallel risk type, the significant volume size for toxicity risk factors, etc.); 
         [0016]    (v) other patient specific features such as clinical and demographic variables; and 
         [0017]    (vi) machine specific features such as the treatment modality and beam angle arrangement; 
         [0000]    and where the output comprises data based on achieved dose distributions for the patient (e.g., three-dimensional dose distributions, dose volume histograms, etc.) that were extracted from training treatment plans. 
         [0018]    In accordance with the illustrative embodiment, an effective method for characterizing the geometry of one organ at risk in relation to the target volume(s) is the distance to target histogram (DTH). This histogram measures the portion of OAR volume that is at a certain distance from the target volume, and vice versa. The distance in DTH may be measured in Euclidean space or in some other non-Euclidean space, in a linear or non-linear manner (e.g., a distance space distorted by the radiation beam geometry or dose deposition characteristics, etc.). The DTH is a two-dimensional representation of the three-dimensional geometry relating one structure (organ or target) to another structure (organ or target). 
         [0019]    During training, the predictive model is trained to identify and infer patterns in the treatment plan data through a process of generalization. Once trained, the predictive model can then be used to automatically generate radiation treatment planning parameters for new patients. These planning parameters may include dose distributions, dose volume histograms, beam configurations, and so forth. 
         [0020]    The illustrated embodiment comprises: receiving by a data-processing system one or more data, wherein at least one of the data is based on a geometric characterization of one or more organs at risk proximate to a target volume of a patient P; and generating by the data-processing system one or more radiation treatment planning parameters for the patient P based on the one or more data. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0021]      FIG. 1  depicts a first illustrative dose volume histogram, in accordance with the illustrative embodiment of the present invention. 
           [0022]      FIG. 2  depicts a second illustrative dose volume histogram, in accordance with the illustrative embodiment of the present invention. 
           [0023]      FIG. 3  depicts an illustrative distance to target histogram characterizing the geometry of one or more organs at risk (OAR) with respect to a target volume, in accordance with the illustrative embodiment of the present invention. 
           [0024]      FIG. 4  depicts the salient elements of automated planning parameter-generation system  400 , in accordance with the illustrative embodiment of the present invention. 
           [0025]      FIG. 5  depicts a block diagram of the salient elements of data-processing system  401 , as shown in  FIG. 4 , in accordance with the illustrative embodiment of the present invention. 
           [0026]      FIG. 6  depicts a block diagram of the salient data stored in plan database  402 , as shown in  FIG. 4 , in accordance with the illustrative embodiment of the present invention. 
           [0027]      FIG. 7  depicts a block diagram of the contents of plan data  601 - i , as shown in  FIG. 6 , in accordance with the illustrative embodiment of the present invention. 
           [0028]      FIG. 8  depicts the contents of organ volume data  701 - i , as shown in  FIG. 7 , in accordance with the illustrative embodiment of the present invention. 
           [0029]      FIG. 9  depicts the contents of geometric characterization  702 - i , as shown in  FIG. 7 , in accordance with the illustrative embodiment of the present invention. 
           [0030]      FIG. 10  depicts the contents of dose volume histogram  703 - i , as shown in  FIG. 7 , in accordance with the illustrative embodiment of the present invention. 
           [0031]      FIG. 11  depicts a flowchart of the salient tasks performed by data-processing system  401 , in accordance with the illustrative embodiment of the present invention. 
           [0032]      FIG. 12  depicts a detailed flowchart of task  1101 , as shown in  FIG. 11 , in accordance with the illustrative embodiment of the present invention. 
           [0033]      FIG. 13  depicts a detailed flowchart of task  1102 , as shown in  FIG. 11 , in accordance with the illustrative embodiment of the present invention. 
           [0034]      FIG. 14  depicts a detailed flowchart of subtask  1302 , as shown in  FIG. 13 , in accordance with the illustrative embodiment of the present invention. 
           [0035]      FIG. 15  depicts a detailed flowchart of subtask  1304 , as shown in  FIG. 13 , in accordance with the illustrative embodiment of the present invention. 
           [0036]      FIG. 16  depicts a detailed flowchart of task  1105 , as shown in  FIG. 11 , in accordance with the illustrative embodiment of the present invention. 
           [0037]      FIG. 17  depicts a detailed flowchart of subtask  1601 , as shown in  FIG. 16 , in accordance with the illustrative embodiment of the present invention. 
           [0038]      FIG. 18  depicts a detailed flowchart of subtask  1604 , as shown in  FIG. 16 , in accordance with the illustrative embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0039]      FIG. 1  depicts first illustrative dose volume histogram (DVH)  100 , referred to as a “differential DVH,” in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 1 , DVH  100  associates each of a plurality of dosage ranges (e.g., 0-2 Gy, 2-4 Gy, etc.) inside the volume of an organ at risk (x-axis) with the percentage of the volume being exposed to that dosage range (y-axis). As will be appreciated by those skilled in the art, in some embodiments of the present invention dose volume histogram  100  might be derived from a dose distribution, while in some other embodiments dose volume histogram  100  might be derived from dose volume histogram  200 , described below and with respect to  FIG. 2 , while in still some other embodiments dose volume histogram  100  might be derived from some other data or obtained in some other manner. 
         [0040]      FIG. 2  depicts second illustrative dose volume histogram (DVH)  200 , referred to as a “cumulative DVH,” in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 2 , DVH  200  associates dosage range (x-axis) with the percentage of an organ or target volume (y-axis, where 1 corresponds to 100% volume and 0 corresponds to 0% volume). As will be appreciated by those skilled in the art, in some embodiments of the present invention dose volume histogram  200  might be derived from dose volume histogram  100 , while in some other embodiments dose volume histogram  200  might be derived directly from a dose distribution, while in still some other embodiments dose volume histogram  200  might be derived from some other data or obtained in some other manner. 
         [0041]      FIG. 3  depicts illustrative graph  300  characterizing the geometry of one or more organs at risk (OAR) with respect to a target volume, in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 3 , graph  300  associates distance from the target volume (x-axis) with a percentage of the total volume of the organ(s) at risk (y-axis), where a negative distance indicates overlap between the target and OAR. For example, point  301  indicates that 85% of the total volume of the organ(s) at risk is within one centimeter of the target volume. The distance can be further defined as linear or nonlinear, using Euclidean or other non-Euclidean metric space. 
         [0042]      FIG. 4  depicts the salient elements of automated planning parameter-generation system  400 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 4 , automated planning parameter-generation system  400  comprises data-processing system  401  and training plan database  402 , interconnected as shown. 
         [0043]    Data-processing system  401  is an apparatus comprising hardware and software (e.g., a server, a desktop computer, a notebook computer, etc.) that is capable of reading from and writing to training plan database  402 , of storing a representation of a predictive model, of training the predictive model, of generating a computer-executable program that applies the trained predictive model, and of executing the tasks described below and with respect to  FIGS. 11 through 15 . Data-processing system  401  is described in detail below and with respect to  FIG. 5 . 
         [0044]    Training plan database  402  is capable of providing persistent storage of data and efficient retrieval of the stored data, in well-known fashion. The contents of plan database are described in detail below and with respect to  FIGS. 6 through 10 . 
         [0045]      FIG. 5  depicts a block diagram of the salient elements of data-processing system  401 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 5 , data-processing system  401  comprises processor  501 , memory  502 , and transceiver  503 , interconnected as shown. 
         [0046]    Processor  501  is a general-purpose processor that is capable of receiving information from transceiver  503 , of reading data from and writing data into memory  502 , of executing instructions stored in memory  502 , and of forwarding information to transceiver  503 , in well-known fashion. As will be appreciated by those skilled in the art, in some alternative embodiments of the present invention processor  501  might be a special-purpose processor, rather than a general-purpose processor. 
         [0047]    Memory  502  is capable of storing data and executable instructions, in well-known fashion, and might be any combination of random-access memory (RAM), flash memory, disk drive, etc. In accordance with the illustrative embodiment, memory  502  stores executable instructions corresponding to the tasks of the methods of  FIGS. 11 through 15  below. 
         [0048]    Transceiver  503  is capable of receiving signals from plan database  402  and forwarding information encoded in these signals to processor  501 , and of receiving information from processor  501  and transmitting signals that encode this information to plan database  402 , in well-known fashion. 
         [0049]      FIG. 6  depicts a block diagram of the salient data stored in plan database  402 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 6 , plan database comprises records  601 - 1  through  601 -D, where each record  601 - i  corresponds to a respective patient and contains data associated with a training treatment plan that was formulated by an expert human planner for the patient using either a trial-and-error approach or a pareto-front guided search process. The contents of record  601 - i  are described in detail below and with respect to  FIGS. 7 through 10 . 
         [0050]      FIG. 7  depicts a block diagram of the contents of record  601 -i, where i is an integer between 1 and D inclusive, in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 7 , record  601 -i comprises: organ volume data  701 - i , which is further described below and with respect to  FIG. 8 ; geometric characterization  702 - i , which is further described below and with respect to  FIG. 9 ; three-dimensional dose distribution  703 - i ; dose volume histogram  703 - i , which is further described below and with respect to  FIG. 10 ; target volume  705 - i ; target dose and DVH prescriptions  706 - i ; OAR dose and DVH sparing prescriptions  707 - i ; physician sparing preferences and characteristics  708 - i  (e.g., limit lung volume receiving at least 10 Gy to less than 5%, meet all sparing goals for single-kidney patient, etc.); machine-specific features  709 - i  (e.g., treatment modality, beam angle arrangement, etc.); and additional patient-specific features  710 - i  (e.g., clinical variables, demographic variables, etc.). 
         [0051]      FIG. 8  depicts the contents of organ volume data  701 - i , where i is an integer between 1 and D inclusive, in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 8 , organ volume data  701 - i  stores: target volume size  801 - i , which is the size (i.e., volume) of the target volume, in appropriate units (e.g., cubic millimeters, etc.); organ at risk (OAR) volume sizes  802 - i ; organ shape descriptions  803 - i ; partial target volumes  804 - i  overlapping one organ; partial target volumes  805 - i  overlapping multiple organs; partial organ volumes  806 - i  overlapping target; partial organ volumes  807 - i  overlapping other organs; and partial organ volumes  808 - i  meeting specific beam configuration descriptions (e.g., partial volumes residing outside primary radiation fields, etc.). 
         [0052]      FIG. 9  depicts the contents of geometric characterization  702 -i, where i is an integer between 1 and D inclusive, in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 9 , geometric characterization  702 - i  comprises two-dimensional points  901 - i - 1  through  901 - i -K, where K is a positive integer, and where each of the points associates distance from the target volume with a percentage of the total volume of the organ(s) at risk. In other words, each of points  901 - i - 1  through  901 - i -K correspond to a point on the type of curve illustrated in  FIG. 3 . 
         [0053]    As will be appreciated by those skilled in the art, geometric characterization  702 -i of the illustrative embodiment covers the tools and methods that can characterize the geometry of one organ at risk in relation to one or more target volumes, and to other organs at risk. One such geometry description tool is the distance to target histogram (DTH), which measures the portion of OAR or target volume that is at a certain distance from the target volume or other organs. The distance in DTH may be measured in Euclidean space or in some other non-Euclidean space, in a linear or non-linear manner (e.g., a distance space distorted by the radiation beam geometry or dose deposition characteristics, etc.). 
         [0054]      FIG. 10  depicts the contents of dose volume histogram  703 -i, where i is an integer between 1 and D inclusive, in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 10 , dose volume histogram  703 - i  comprises two-dimensional points  1001 - i - 1  through  1001 - i -L, where L is a positive integer, and where each of the points is taken from the dose volume histogram for the patient. As described above, in some embodiments of the present invention, each of points  1001 - i - 1  through  1001 - i -L might associate dosage ranges with a percentage of the volume being exposed to that dosage range (e.g., points corresponding to the histogram bins of illustrative DVH  100  in  FIG. 1 , etc.), while in some other embodiments, each of points  1001 - i - 1  through  1001 - i -L might associate dose value with a percentage of the volume being exposed to that dose or higher (e.g., points corresponding to those of illustrative DVH  200  in  FIG. 2 , etc.), while in still some other embodiments, each of points  1001 - i - 1  through  1001 - i -L might be obtained from some other type of representation of the dose volume histogram for the patient. 
         [0055]      FIG. 11  depicts a flowchart of the salient tasks performed by data-processing system  401 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which tasks depicted in  FIG. 11  can be performed simultaneously or in a different order than that depicted. 
         [0056]    At task  1101 , data-processing system  401  populates plan database  402 . Task  1101  is described in detail below and with respect to  FIG. 12 . 
         [0057]    At task  1102 , data-processing system  401  trains a predictive model on the data in plan database  402 . Task  1102  is described in detail below and with respect to  FIG. 13 . 
         [0058]    At task  1103 , data-processing system  401  generates an executable program based on the trained predictive model, in well-known fashion. 
         [0059]    At task  1104 , data-processing system  401  receives data for a patient P for whom a radiation treatment plan is desired. In accordance with the illustrative embodiment, these data include, but are not limited to: 
         [0060]    the size and shape of patient P′s target volume; 
         [0061]    the size(s) and shape(s) of each of patient P′s organ(s) at risk; and 
         [0062]    a geometric characterization (of the form of the illustrative curve depicted in  FIG. 3 ) of patient P′s organ(s) at risk with respect to the target volume. 
         [0063]    At task  1105 , data-processing system  401  generates a set of radiation treatment planning parameters for patient P. Task  1105  is described in detail below and with respect to  FIGS. 16 through 18 . 
         [0064]    After task  1105  has been completed, execution continues back at task  1104 . 
         [0065]      FIG. 12  depicts a detailed flowchart of task  1101 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 12  can be performed simultaneously or in a different order than that depicted. 
         [0066]    At subtask  1201 , data-processing system  401  initializes variable S to a set of radiation treatment plans previously formulated by expert human planners using trial-and-error approach or pareto-front guided search. 
         [0067]    At subtask  1202 , data-processing system  401  initializes variable i to  1 . 
         [0068]    At subtask  1203 , data-processing system  401  selects from set S a plan for a patient P. 
         [0069]    At subtask  1204 , data-processing system  401  stores the size of patient P′s target volume and the size(s) of patient P′s organ(s) at risk in organ volume data  701 - i  of patient record  601 - i  in plan database  402 . 
         [0070]    At subtask  1205 , data-processing system  401  extracts a set of points from a geometric characterization of patient P′s organ(s) at risk and his or her target volume. 
         [0071]    At subtask  1206 , data-processing system  401  stores the set of points obtained at subtask  1205  in geometric characterization  702 - i  of patient record  601 - i  in plan database  402 . 
         [0072]    At subtask  1207 , data-processing system  401  extracts a set of points from a dose volume histogram and dose points meeting other specific geometric characteristics that were formulated for patient P by either an expert human planner or computerized pareto-optimal plans. 
         [0073]    At subtask  1208 , data-processing system  401  stores the set of points obtained at subtask  1207  in dose volume histogram  703 - i  of patient record  601 - i  in plan database  402 . 
         [0074]    At subtask  1209 , data-processing system  401  removes patient P from set S. 
         [0075]    At subtask  1210 , data-processing system  401  checks whether set S is empty; if so, execution continues at task  1102  of  FIG. 11 , otherwise execution proceeds to subtask  1210 . 
         [0076]    At subtask  1211 , data-processing system  401  increments variable i. After subtask  1210 , execution continues back at subtask  1203 . 
         [0077]      FIG. 13  depicts a detailed flowchart of task  1102 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 13  can be performed simultaneously or in a different order than that depicted. 
         [0078]    At subtask  1301 , data-processing system  401  selects some record  601 -i from plan database  402 , where i is an integer between 1 and D inclusive. 
         [0079]    At subtask  1302 , data-processing system  401  performs a feature selection algorithm on geometric characterization  702 - i  of patient record  601 - i  with respect to the geometric characterizations of other patients. In accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm at subtask  1302 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature selection algorithm might be employed at subtask  1302 , and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. 
         [0080]    Subtask  1302  is described in detail below and with respect to  FIG. 14 . 
         [0081]    At subtask  1303 , data-processing system  401  generates an input vector that comprises (i) one or more values based on the output data of the principal component analysis of subtask  1302 , (ii) target size  801 - i  of patient record  601 - i , and (iii) and organ at risk (OAR) sizes  802 - i - 1  through  802 - i -R of patient record  601 - i . As will be appreciated by those skilled in the art, in some embodiments of the present invention the one or more values of item (i) might simply be the principal component scores corresponding to the M eigenvalues obtained at subtask  1302 , while in some other embodiments the one or more values of item (i) might be derived in some way from these M eigenvalues (e.g., via normalization of the eigenvalues, via a technique that combines the eigenvalues in some fashion, etc.). 
         [0082]    At subtask  1304 , data-processing system  401  performs a feature selection algorithm on: 
         [0083]    (i) dose volume histogram  703 - i  of patient record  601 - i , and 
         [0084]    (ii) dose points meeting other specific geometric characteristics with respect to the dose volume histograms of other patients. In accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm at subtask  1304 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature reduction algorithm might be employed at subtask  1304 , and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. 
         [0085]    Subtask  1304  is performed in a manner similar to subtask  1302 , and is described in detail below and with respect to  FIG. 15 . 
         [0086]    At subtask  1305 , data-processing system  401  generates an output vector that is based on the output data of the principal component analysis of subtask  1304 . As will be appreciated by those skilled in the art, in some embodiments of the present invention the output vector might simply contain principal component scores corresponding to the Q eigenvalues obtained at subtask  1304 , while in some other embodiments the output vector might be derived in some way from these Q eigenvalues (e.g., via normalization of the eigenvalues, via a technique that combines the eigenvalues in some fashion, etc.). 
         [0087]    At subtask  1306 , data-processing system  401  trains the predictive model on the input vector and output vector generated at subtasks  1303  and  1305 , respectively. 
         [0088]    At subtask  1307 , data-processing system  401  determines whether the predictive model has been trained sufficiently. As will be appreciated by those skilled in the art, in some embodiments of the present invention this determination might be based on one or more convergence criteria, while in some other embodiments of the present invention the determination might be made in some other fashion (e.g., based on some other criteria, based on a pre-determined number of iterations, etc.). 
         [0089]    If the determination at subtask  1307  is negative, execution continues back at subtask  1301 ; otherwise, execution proceeds to task  1103  of  FIG. 11 . 
         [0090]      FIG. 14  depicts a detailed flowchart of subtask  1302 , in accordance with the illustrative embodiment of the present invention. As noted above, in accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm in the subtasks of  FIG. 14 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature selection algorithm might be employed, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. It will further be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 14  can be performed simultaneously or in a different order than that depicted. 
         [0091]    At subtask  1401 , data-processing system  401  constructs an N-by-N covariance matrix of all feature points across all training plans, in well known fashion, where N is a positive integer equal to K. 
         [0092]    At subtask  1402 , data-processing system  401  computes the eigenvalues of the N-by-N covariance matrix, in well-known fashion. 
         [0093]    At subtask  1403 , data-processing system  401  selects the M largest of the eigenvalues computed at subtask  1402 , where M is a positive integer between 1 and N inclusive, and returns the principal component scores associated with selected eigenvalues as outputs to subtask  1303  of  FIG. 13 . After subtask  1403 , execution continues at subtask  1303 . 
         [0094]      FIG. 15  depicts a detailed flowchart of subtask  1304 , in accordance with the illustrative embodiment of the present invention. As noted above, in accordance with the illustrative embodiment, a principal component analysis is employed as the feature reduction algorithm in the subtasks of  FIG. 15 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature reduction algorithm might be employed, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature reduction algorithms. It will further be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 15  can be performed simultaneously or in a different order than that depicted. 
         [0095]    At subtask  1501 , data-processing system  401  constructs a Z-by-Z covariance matrix of Z sample points of dose volume histograms across all plans, in well known fashion, where Z is a positive integer equal to L. As will be appreciated by those skilled in the art, in some embodiments of the present invention the value of Z might be the same as the value of N used at subtask  1401 , while in some other embodiments of the present invention, Z might have a different value than N. 
         [0096]    At subtask  1502 , data-processing system  401  computes the eigenvalues of the Z-by Z covariance matrix, in well-known fashion. 
         [0097]    At subtask  1503 , data-processing system  401  selects the Q largest of the eigenvalues computed at subtask  1502 , where Q is a positive integer between 1 and Z inclusive, and returns the principal component scores associated with the selected eigenvalues as outputs to subtask  1305  of  FIG. 13 . After subtask  1503 , execution continues at subtask  1305 . 
         [0098]      FIG. 16  depicts a detailed flowchart of task  1105 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 16  can be performed simultaneously or in a different order than that depicted. 
         [0099]    At subtask  1601 , data-processing system  401  performs a feature selection algorithm on the geometric characterization  702 - i  for patient P (received at task  1104 ) with respect to the geometric characterizations of other patients. As noted above, in accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm in the subtasks of  FIG. 16 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature selection algorithm might be employed, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. 
         [0100]    Subtask  1601  is described in detail below and with respect to  FIG. 17 . 
         [0101]    At subtask  1602 , data-processing system  401  generates an input vector that contains (i) one or more values based on the output data of the principal component analysis of subtask  1601 , (ii) the size and shape of patient P′s target volume, and (iii) the size(s) and shape(s) of patient P′s organ(s) at risk. As will be appreciated by those skilled in the art, in some embodiments of the present invention the one or more values of item (i) might simply be the principal component scores corresponding to the M eigenvalues obtained at subtask  1601 , while in some other embodiments the one or more values of item (i) might be derived in some way from these M eigenvalues (e.g., via normalization of the eigenvalues, via a technique that combines the eigenvalues in some fashion, etc.). 
         [0102]    At subtask  1603 , data-processing system  401  runs the executable program generated at task  1103  on the input vector and obtains an output vector. 
         [0103]    At subtask  1604 , data-processing system  401  generates radiation treatment planning parameters for patient P based on the output vector. Subtask  1604  is described in detail below and with respect to  FIG. 18 . 
         [0104]    After subtask  1604 , execution continues back at task  1104 . 
         [0105]      FIG. 17  depicts a detailed flowchart of subtask  1601 , in accordance with the illustrative embodiment of the present invention. As noted above, in accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm in the subtasks of  FIG. 17 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature selection algorithm might be employed, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. It will further be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 17  can be performed simultaneously or in a different order than that depicted. 
         [0106]    At subtask  1701 , data-processing system  401  extracts a set of N points from the geometric characterization for patient P. 
         [0107]    At subtask  1702 , data-processing system  401  uses the N-by-N covariance matrix, in well known fashion. 
         [0108]    At subtask  1703 , data-processing system  401  computes the eigenvalues, in well-known fashion. 
         [0109]    At subtask  1704 , data-processing system  401  computes the principal component scores corresponding to the M largest of the eigenvalues computed at subtask  1603  and returns the principal component scores as outputs to subtask  1602  of  FIG. 16 . After subtask  1704 , execution continues at subtask  1602 . 
         [0110]      FIG. 18  depicts a detailed flowchart of subtask  1604 , in accordance with the illustrative embodiment of the present invention. As noted above, in accordance with the illustrative embodiment, a principal component analysis is employed as the feature selection algorithm in the subtasks of  FIG. 18 ; however, as will be appreciated by those skilled in the art, in some other embodiments of the present invention some other type of feature selection algorithm might be employed, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of the present invention that employ such alternative feature selection algorithms. It will further be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 18  can be performed simultaneously or in a different order than that depicted. 
         [0111]    At subtask  1801 , data-processing system  401  arranges the eigenvectors computed at subtask  1704  into a matrix that corresponds to the eigenvalues in descending order. 
         [0112]    At subtask  1802 , data-processing system  401  inverts the matrix constructed at subtask  1801 , in well-known fashion. 
         [0113]    At subtask  1803 , data-processing system  401  computes a dose volume histogram (DVH) and other dose-points from the inverted matrix using the output scores of the principal components. 
         [0114]    After subtask  1803 , execution continues back at task  1104 . 
         [0115]    As will be appreciated by those skilled in the art, although the illustrative embodiment is disclosed in the context of a single target volume, the techniques of the illustrative embodiment can easily be adapted by one skilled in the art to accommodate patients having a plurality of target volumes. 
         [0116]    As will further be appreciated by those skilled in the art, although the illustrative embodiment employs principal component analysis as the feature selection algorithm, some other embodiments of the present invention might employ some other type of feature selection algorithm, and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. 
         [0117]    As will yet further be appreciated by those skilled in the art, although the geometric characterizations of the illustrative embodiment may be expressed as distances in Euclidean space, the distances are in fact general measurements that may be expressed in some other type of space (e.g., a distance space distorted by radiation beam geometry, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments that employ such alternative distance spaces. 
         [0118]    As will still further be appreciated by those skilled in the art, although the illustrative embodiment is disclosed in the context of general intensity-modulated radiation therapy (IMRT), the techniques of the illustrative embodiment can be employed for both static gantry angle intensity-modulated radiation therapy (IMRT) and rotation gantry volumetric modulated arc therapy (VMAT), as well as other types of radiation therapy. 
         [0119]    It is to be understood that the disclosure teaches just one example of the illustrative embodiment and that many variations of the invention can easily be devised by those skilled in the art after reading this disclosure and that the scope of the present invention is to be determined by the following claims.