Abstract: Systems and methods for assessing patient-specific evolution of resistance to therapy and progression of disease in recurrent high-grade glioma patients are described herein. An example method includes receiving a plurality of patient-specific parameters for a patient having recurrent high-grade glioma. The patient-specific parameters include an evolution of resistance rate to a In combination therapy, a pre-treatment tumor volume, and a radiation surviving fraction. The method also includes simulating, for each of a plurality of radiation therapy protocols, a respective volumetric tumor growth trajectory for the patient. The simulation is performed using a tumor growth model based on the patient-specific parameters. The method further includes determining an optimal radiation therapy protocol based on the simulation, wherein the optimal radiation therapy prolongs progression of the recurrent high-grade glioma.

Abstract: Systems and methods utilizing a Bayesian framework for tumor forecasting are described herein. An example method may include: inputting a plurality of patient data for a patient into a multi-model framework; predicting, using the multi-model framework, a probability of a given treatment producing a given outcome for the patient; and outputting an assessment for the given treatment.

Abstract: Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes 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. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

Abstract: Disclosed are methods for personalized treatment of tumor lesions in subject following primary tumor treatment. Disclosed are methods related to using patient specific prostate cancer dynamic model to adjust a patient's intermittent androgen deprivation therapy to determine when to pause IADT, when to resume IADT, the amount of treatment, and the rate of treatment and thereby increasing the time to progression of the tumor and thus prolonging post cancer treatment survival.

Abstract: Disclosed are methods for assessing radiosensitivity based on individual radiation immune sensitivity (iRIS) as a metic to predict radiation response based on its effect on the tumor-immune ecosystem (TIES).

Abstract: Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.

Abstract: Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.

Abstract: Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.

Abstract: Methods for personalized treatment of tumor lesions in subject with metastatic cancer are disclosed.