Radiation therapy planning using integrated model

Abstract

System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 accessing patient information for a patient;   accessing an integrated dose prediction model that integrates a plurality of predictive models, wherein said integrated dose prediction model is a hierarchical model comprising said plurality of predictive models arranged in a hierarchy, and wherein a respective predictive model of said plurality of predictive models is operable for determining a dose distribution in said patient using said patient information;   selecting one or more predictive models of said plurality of predictive models with said integrated dose prediction model;   processing said patient information with said one or more predictive models; and   outputting, by said one or more predictive models, said dose distribution.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein said one or more predictive models are selected based on said patient information. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein said selecting and said processing comprise:
 generating radiation treatment predictions based on said patient information using said plurality of predictive models;   evaluating said radiation treatment predictions; and   selecting said one or more predictive models based on said evaluating.   
     
     
         4 . The computer-implemented method of  claim 3 , wherein said evaluating comprises evaluating parameters representing reliability, complexity, and probability with respect to said radiation treatment predictions. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein said patient information comprises a plurality of sets of features; wherein each predictive model of said plurality of predictive models is associated with a respective category with respect to each set of features of said plurality of sets of features; and wherein said selecting comprises:
 identifying a corresponding category in each set of features based on said patient information; and   selecting said one or more predictive models based on identified categories of said plurality of sets of features.   
     
     
         6 . The computer-implemented method of  claim 1 , wherein said patient information comprises a plurality of sets of features; and wherein said plurality of sets of features are selected from a group consisting of: organ type, organ dimension descriptions, target location, target size, and geometric characterizations of one or more organs at risk proximate to a target volume. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein said hierarchy comprises a plurality of intermediate levels and a level comprising said plurality of predictive models; wherein each intermediate level of said plurality of intermediate levels corresponds to categories with respect to a respective set of features; and wherein categories corresponding to a subordinate intermediate level are nested with categories corresponding to a superordinate intermediate level. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein said integrated dose prediction model is generated by classifying said plurality of predictive models based on clinical data in accordance with a clustering algorithm. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein each predictive model of said plurality of predictive models is trained through a machine training process in accordance with a regression method and based on clinical data. 
     
     
         10 . A non-transitory computer-readable storage medium embodying instructions that, when executed by a processing device, cause said processing device to perform a method comprising:
 accessing a plurality of predictive models, wherein each predictive model of said plurality of predictive models is operable for determining a dose distribution in a patient; and   integrating said plurality of predictive models into said integrated dose prediction model, wherein said integrated dose prediction model is a hierarchical model comprising said plurality of predictive models arranged in a hierarchy, wherein said integrated dose prediction model is configured to:
 receive patient information for said patient; 
 select one or more predictive models from said plurality of predictive models; and 
 process said patient information with said one or more predictive models to output said dose distribution. 
   
     
     
         11 . The non-transitory computer-readable storage medium of  claim 10 , wherein said method further comprises:
 configuring an input interface operable to receive said patient information; and   configuring an output interface operable to output said dose distribution.   
     
     
         12 . The non-transitory computer-readable storage medium of  claim 10 , wherein said patient information comprises features selected from a group consisting of: organ identification, organ dimension descriptions, target location, target size, and geometric characterizations of one or more organs at risk proximate to a target volume. 
     
     
         13 . The non-transitory computer-readable storage medium of  claim 10 , wherein said integrating comprises associating a category with said patient information. 
     
     
         14 . The non-transitory computer-readable storage medium of  claim 13 ; wherein said hierarchy comprises a first level and one or more decision levels; wherein said first level comprises said plurality of predictive models; and wherein said one or more decision levels are operable to select said one or more predictive models from said first level based on said category. 
     
     
         15 . The non-transitory computer-readable storage medium of  claim 10 , wherein said method further comprises:
 accessing training data; and   generating said plurality of predictive models based on said training data in a machine training process, wherein a respective predictive model of said plurality of predictive models is generated in accordance with an algorithm selected from a group consisting of: a linear regression algorithm, a classification algorithm, a decision tree algorithm, a segmentation algorithm, an association algorithm, a sequence clustering algorithm, and a combination of algorithms in said group.   
     
     
         16 . The non-transitory computer-readable storage medium of  claim 15 , wherein said method further comprises classifying said training data into subsets of training data; and wherein said generating said plurality of predictive models comprises configuring a predictive model of said plurality of predictive models based on a subset of said training data. 
     
     
         17 . A system, comprising:
 a processor;   a memory coupled to said processor and comprising instructions that, when executed by said processor, cause said processor to perform a method comprising:
 accessing patient information; 
 accessing an integrated dose prediction model that integrates a plurality of predictive models, wherein said integrated dose prediction model is a hierarchical model comprising said plurality of predictive models arranged in a hierarchy, wherein a respective predictive model of said plurality of predictive models is operable for determining a dose distribution in a patient using said patient information; 
 selecting one or more predictive models of said plurality of predictive models with said integrated dose prediction model; 
 processing said patient information with said one or more predictive models; and 
 outputting, by said one or more predictive models, said dose distribution. 
   
     
     
         18 . The system of  claim 17 , wherein said selecting and said processing comprise:
 generating radiation treatment predictions based on said patient information using said plurality of predictive models;   evaluating said radiation treatment predictions; and   selecting said one or more predictive models based on said evaluating.   
     
     
         19 . The system of  claim 17 , wherein said hierarchy comprises a plurality of intermediate levels and a level comprising said plurality of predictive models; wherein each intermediate level of said plurality of intermediate levels corresponds to categories with respect to a respective set of features; and wherein categories corresponding to a subordinate intermediate level are nested with categories corresponding to a superordinate intermediate level. 
     
     
         20 . The system of  claim 17 , wherein said patient information comprises a plurality of sets of features; wherein each predictive model of said plurality of predictive models is associated with a respective category with respect to each set of features of said plurality of sets of features; and wherein said selecting comprises:
 identifying a corresponding category in each set of features of said plurality of sets of features based on said patient information; and   selecting said one or more predictive models based on identified categories of said plurality of sets of features.