SYSTEMS AND METHODS FOR SPECIFYING TREATMENT CRITERIA AND TREATMENT PARAMETERS FOR PATIENT SPECIFIC RADIATION THERAPY PLANNING

• US 20160129282A1
• Filed: 06/18/2014
• Published: 05/12/2016
• Est. Priority Date: 06/18/2013
• Status: Active Grant

• Current Assignee: Duke University, Wake Forest University Health Sciences
• Original Assignee: Duke University
• Inventors: Yuan, Lulin, Ge, Yaorong, Yin, Fang-Fang, Wu, Qingrong Jackie
• Granted Patent: US 10,449,388 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61N 2005/1074 Details of the control system, e.g. user interfaces
• CPC Class Codes: A61N 5/103 Treatment planning systems
• CPC Class Codes: A61N 5/1031 using a specific method of dose optimization
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

Disclosed herein are systems and methods for specifying treatment criteria and treatment planning parameters for patient specific radiation therapy planning. According to an aspect, a method includes receiving data about a patient, computing geometric characterization of one or more organs at risk proximate to a target volume of a patient or vice versa, and selecting relevant treatment knowledge and experience. The method also includes generating, based on the received data, computed geometric characterization, and available knowledge and experience, a first set of radiation treatment planning parameters that will lead to a high quality plan for the patient. Further, the method includes model-based prediction, based on the data, a second set or more of radiation treatment planning parameters that will lead to alternative achievable plans with different organ sparing objectives for treating the patient. The multiple sets for parameters can be used separately or in conjunction to generate treatment plans.

Claims

1. A method comprising:

at a processor and memory;

receiving data based on patient information and geometric characterization of one or more organs at risk proximate to a target volume of a patient;

generating, based on the data, a first set of radiation treatment planning parameters for the patient in a treatment model;

generating, based on the data, a second set of radiation treatment planning parameters for treating the patient in the treatment model, the first set of radiation treatment planning parameters being different than the second set of radiation treatment planning parameters; and

presenting the first and second radiation treatment planning parameters via a user interface.

2. The method of claim 1, wherein the patient information includes one or more of patient image, patient organ contour information, target volume contour information, and clinical parameters.

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

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

5. The method of claim 1, wherein the data comprises information about one of radiation treatment knowledge, experience, and preferences, and computerized models of published clinical trials results and guidelines.

6. The method of claim 1, further comprising computing the geometric characterization of the patient.

7. The method of claim 1, wherein the radiation treatment planning parameters are represented by at least one of a dose distribution and a dose volume histogram.

8. The method of claim 1, wherein generating a first set of radiation treatment planning parameters comprises generating the first set of radiation treatment planning parameters based on a predictive model.

9. The method of claim 1, wherein generating a second set of radiation treatment planning parameters comprises generating the second set of radiation treatment planning parameters based on a predictive model.

10. The method of claim 1, wherein the data includes a geometric characterization of the target volume with respect to one or more organs at risk.

11. The method of claim 1, further comprising storing the data.

12. The method of claim 1, further comprising training 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 dose volume histograms for a respective patient, and wherein the input of each input-output mapping comprises one or more data that are based on the geometric characterization.

13. A system comprising:

at least one processor and memory configured to;

receive data based on patient information and geometric characterization of one or more organs at risk proximate to a target volume of a patient;

generate, based on the data, a first set of radiation treatment planning parameters for the patient in a treatment model; and

generate, based on the data, a second set of radiation treatment planning parameters for treating the patient in the treatment model, the first set of radiation treatment planning parameters being different than the second set of radiation treatment planning parameters; and

a user interface configured to present the first and second radiation treatment planning parameters.

14. The system of claim 13, wherein the patient information includes one or more of patient image, patient organ contour information, target volume contour information, and clinical parameters.

15. The system of claim 13, wherein the geometric characterization associates each of a plurality of distances from the target volume with a respective percentage for the volume of the one or more organs at risk.

16. The system of claim 13, wherein the data comprises the size of the target volume and the respective sizes and shapes of the one or more organs at risk.

17. The system of claim 13, wherein the data comprises information about one of knowledge, experience, and preferences, and computerized models of published clinical trials results and guidelines.

18. The system of claim 13, wherein the at least one processor and memory are configured to are configured to compute the geometric characterization of the patient.

19. The system of claim 13, wherein the radiation treatment planning parameters are represented by at least one of a dose distribution and a dose volume histogram.

20. The system of claim 13, wherein the at least one processor and memory configured to are configured to generate the first set of radiation treatment planning parameters based on a predictive model.

21. The system of claim 13, wherein the at least one processor and memory are configured to generate the second set of radiation treatment planning parameters based on a predictive model.

22. The method of claim 13, wherein the data includes a geometric characterization of the target volume with respect to one or more organs at risk.

23. The method of claim 13, wherein the at least one processor and memory are configured to configured to store the data.

24. The method of claim 13, wherein the at least one processor and memory configured to train 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 dose volume histograms for a respective patient, and wherein the input of each input-output mapping comprises one or more data that are based on the geometric characterization.

25. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions by a computing device to cause the computing device to:

receive, by the computing device, data based on patient information and geometric characterization of one or more organs at risk proximate to a target volume of a patient;

generate, by the computing device and based on the data, a first set of radiation treatment planning parameters for the patient in a treatment model; and

generate, by the computing device and based on the data, a second set of radiation treatment planning parameters for treating the patient in the treatment model, the first set of radiation treatment planning parameters being different than the second set of radiation treatment planning parameters; and

present, by the computing device and based on the data, the first and second radiation treatment planning parameters.