US 12,406,772 B2
Systems and methods for predicting individual patient response to radiotherapy using a dynamic carrying capacity model
Heiko Enderling, New Tampa, FL (US); and Mohammad Zahid, Tampa, FL (US)
Assigned to H. LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE, INC., Tampa, FL (US)
Appl. No. 17/786,326
Filed by H. LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE, INC., Tampa, FL (US)
PCT Filed Dec. 18, 2020, PCT No. PCT/US2020/065942
§ 371(c)(1), (2) Date Jun. 16, 2022,
PCT Pub. No. WO2021/127392, PCT Pub. Date Jun. 24, 2021.
Claims priority of provisional application 63/010,327, filed on Apr. 15, 2020.
Claims priority of provisional application 62/950,296, filed on Dec. 19, 2019.
Prior Publication US 2023/0038942 A1, Feb. 9, 2023
Int. Cl. G16H 50/20 (2018.01); G06T 7/00 (2017.01); G06T 7/62 (2017.01); G16H 20/40 (2018.01)
CPC G16H 50/20 (2018.01) [G06T 7/0016 (2013.01); G06T 7/62 (2017.01); G16H 20/40 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/30096 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving at least two images of a target patient's tumor including a first image captured at a first time point and a second image captured at a second time point;
deriving respective values for tumor volume of the target patient's tumor at the first time point and the second time point from the at least two images;
calculating a change in tumor volume between the first and second time points based on the respective values for tumor volume;
estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume between the first and second time points;
predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity; and
predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patient's tumor at the future time point.
 
12. A system, comprising:
a processor; and
a memory operably coupled to the processor, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to:
receiving at least two images of a target patient's tumor including a first image captured at a first time point and a second image captured at a second time point;
derive respective values for tumor volume of the target patient's tumor at the first time point and the second time point;
calculate a change in tumor volume between the first and second time points based on the respective values for tumor volume;
estimate a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume between the first and second time points;
predict a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity; and
predict a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patient's tumor at the future time point.