Systems and methods for specifying treatment criteria and treatment parameters for patient specific radiation therapy planning

• US 10,449,388 B2
• Filed: 06/18/2014
• Issued: 10/22/2019
• Est. Priority Date: 06/18/2013
• Status: Active Grant

• Current Assignee: Duke University, Wake Forest University Health Sciences
• Original Assignee: Duke University
• Inventors: Yin, Fang-Fang, Wu, Qingrong Jackie, Yuan, Lulin, Ge, Yaorong
• Primary Examiner(s): Gilbert, Samuel G
• Application Number:
  US14/893,055
• Publication Number:
  US 20160129282A1
• Time in Patent Office: 1,952 Days
• Field of Search: 600 1, 378 65
• 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 and a predictive model, a first set of radiation treatment planning parameters for the patient, wherein the predictive model is integrated with a computerized model of clinical trial data;

extracting, a set of anatomical points from the geometric characterization of the one or more organs at risk and from the target volume of the patient;

extracting a set of mean dose volume points from a dose volume histogram of the patient;

generating, based on the data, the extracted set of anatomical points, the extracted set of mean dose volume points, and the predictive model, a second set of radiation treatment planning parameters for treating the patient, the first set of radiation treatment planning parameters being different than the second set of radiation treatment planning parameters;

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 a volume of the one or more organs at risk.

4. The method of claim 1, wherein the data comprises a size of the target volume and 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 the dose volume histogram.

8. 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.

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

10. The method of claim 1, further comprising training the predictive model on a plurality of input-output mappings, wherein an output of each input-output mapping is based on a dose distribution and the dose volume histogram for the respective patient, and wherein an input of each input-output mapping comprises one or more data that are based on the geometric characterization.

11. 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 and a predictive model, a first set of radiation treatment planning parameters for the patient, wherein the predictive model is integrated with a computerized model of clinical trial data;

extract, a set of anatomical points from the geometric characterization of the one or more organs at risk and from the target volume of the patient;

extract a set of mean dose volume points from a dose volume histogram of the patient;

and

generate, based on the data, the extracted first set of anatomical points, the extracted set of mean dose volume points, and the predictive model, a second set of radiation treatment planning parameters for treating the patient, 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.

12. The system of claim 11, wherein the patient information includes one or more of patient image, patient organ contour information, target volume contour information, and clinical parameters.

13. The system of claim 11, 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.

14. The system of claim 11, wherein the data comprises an size of the target volume and respective sizes and shapes of the one or more organs at risk.

15. The system of claim 11, wherein the data comprises information about one of knowledge, experience, and preferences, and computerized models of published clinical trials results and guidelines.

16. The system of claim 11, wherein the at least one processor and memory are configured to compute the geometric characterization of the patient.

17. The system of claim 11, wherein the first set and second set of radiation treatment planning parameters are represented by at least one of a dose distribution and the dose volume histogram.

18. The system of claim 11, wherein the data includes a geometric characterization of the target volume with respect to one or more organs at risk.

19. The system of claim 11, wherein the at least one processor and memory are configured to store the data.

20. The system of claim 11, wherein the at least one processor and memory configured to train a predictive model on a plurality of input-output mappings, wherein an output of each input-output mapping is based on a dose distribution and the dose volume histogram for the respective patient, and wherein an input of each input-output mapping comprises one or more data that are based on the geometric characterization.

21. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein execution of the program instructions by a computing device causes 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, based on the data and a predictive model, a first set of radiation treatment planning parameters for the patient in a treatment model, wherein the predictive model is integrated with a computerized model of clinical trial data;

extract, by the computing device, a set of anatomical points from the geometric characterization of the one or more organs at risk and from the target volume of the patient;

extract, by the computing device, a set of mean dose volume points from a dose volume histogram of the patient;

and

generate, by the computing device and based on the data, the extracted set of anatomical points, the extracted set of mean dose volume points, second set of dose volume points, and the predictive model, a second set of radiation treatment planning parameters for treating the patient, 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.