Automatic Generation of Patient-Specific Radiation Therapy Planning Parameters

• US 20120014507A1
• Filed: 07/18/2011
• Published: 01/19/2012
• Est. Priority Date: 07/16/2010
• Status: Active Grant

• Current Assignee: Duke University
• Original Assignee: Duke University
• Inventors: Ge, Yaorong, Yin, Fang-Fang, Wu, Qingrong Jackie, Zhu, Xiaofeng
• Granted Patent: US 8,976,929 B2
• Time in Patent Office: Days
• US Class Current: 378/65
• CPC Class Codes: A61N 2005/1041 using a library of previously administered radiation treatment applied to other patients
• CPC Class Codes: A61N 5/10 X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy A61N5/01 takes precedence
• CPC Class Codes: A61N 5/1031 using a specific method of dose optimization

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.

Claims

1. A method comprising:

receiving by a data-processing system one or more data, wherein at least one of said 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 said data-processing system one or more radiation treatment planning parameters for said patient P based on said one or more data.

2. The method of claim 1 wherein said geometric characterization associates each of a plurality of distances from said target volume with a respective percentage of the volume of said one or more organs at risk.

3. The method of claim 1 wherein said one or more data also comprises the size of said target volume and the respective sizes and shapes of said one or more organs at risk.

4. The method of claim 1 wherein said one or more radiation treatment planning parameters are represented by at least one of a dose distribution and a dose volume histogram.

5. The method of claim 1 wherein the generation of said one or more radiation treatment planning parameters is also based on a predictive model.

6. The method of claim 1 wherein at least one of said data is also based on a geometric characterization of said target volume with respect to said one or more organs at risk.

7. The method of claim 1 wherein said one or more data are derived via a feature selection algorithm that is applied to a larger set of data that represent geometric characterizations of one or more organs at risk proximate to a target volume.

8. The method of claim 7 wherein said feature selection algorithm comprises a principal component analysis, and wherein said principal component analysis is based on an N-by-N covariance matrix, and wherein N is a positive integer greater than one.

9. The method of claim 8 wherein said one or more data are based on the M largest eigenvalues of said covariance matrix, and wherein M is a positive integer between 1 and N inclusive.

10. The method of claim 1 wherein said data-processing system employs a predictive model that has been trained on a set of input-output pairs, and wherein the output of each input-output pair is based on a dose distribution and a dose volume histogram for a respective patient, and wherein the input of each input-output pair comprises one or more data that are based on the geometric characterization for said respective patient.

11. A method comprising training by a data-processing system a predictive model on a plurality of input-output mappings, wherein the output of each input-output mapping is based on a dose distribution and a dose volume histogram for a respective patient, and wherein the input of each input-output mapping comprises one or more data that are based on a geometric characterization of one or more organs at risk proximate to a target volume of the respective patient.

12. The method of claim 11 further comprising generating a computer-executable program based on the trained predictive model.

13. The method of claim 11 wherein said geometric characterization associates each of a plurality of distances from said target volume with a respective percentage of the volume of said one or more organs at risk.

14. The method of claim 11 wherein said one or more data also comprises the size of one or more partial volumes.

15. The method of claim 11 wherein said geometric characterization associates a three-dimensional point in said target and one or more organs at risk with a set of values that represent the unique relationship between the point and the target and the organs at risk.

16. The method of claim 11 wherein the output of each input-output mapping is derived via a feature selection algorithm that is applied to dose volume histograms for a plurality of patients.

17. The method of claim 16 wherein said feature selection algorithm comprises a principal component analysis, and wherein said principal component analysis is based on an Z by Z covariance matrix, and wherein Z is the plurality of points from a dose volume histogram for a respective patient, and wherein Z is a positive integer greater than one.

18. The method of claim 17 wherein the output of an input-output mapping comprises the Q largest eigenvalues of said covariance matrix, and wherein Q is a positive integer between 1 and Z inclusive.

19. A method comprising storing in a database, by a data-processing system:

(a) one or more planning parameters of a first radiation treatment plan, wherein said first radiation treatment plan is for a first patient and is generated by one of;

(i) an expert human planner, and

(ii) a data-processing system using one or both of multi-objective optimization and pareto front search;

(b) a first geometric characterization of a first non-empty set of organs at risk proximate to a first target volume of said first patient;

(c) one or more planning parameters of a second radiation treatment plan, wherein said second radiation treatment plan is for a second patient and is generated by one of;

(i) an expert human planner, and

(ii) a data-processing system using one or both of multi-objective optimization and pareto front search; and

(d) a second geometric characterization of a second non-empty set of organs at risk proximate to a second target volume of said second patient.

20. The method of claim 19 further comprising:

generating by said data-processing system;

(i) a first input-output mapping based on said first geometric characterization and on said one or more planning parameters of said first radiation treatment plan, and

(ii) a second input-output mapping based on said second geometric characterization and on said one or more planning parameters of said second radiation treatment plan; and

training by said data-processing system a predictive model on said first input-output mapping and said second input-output mapping.