Motivation: Cervical cancer patients have a 20-30% risk of residual disease after standard-of-care chemoradiation, so there is a need for optimized therapeutic interventions. Goal(s): We aim to predict response of individual cervical cancer patients to chemoradiation using a mathematical model calibrated with quantitative MRI data. Approach: We calibrated a reaction-diffusion model of tumor cellularity with MRI data before (V1) and two weeks into chemoradiation (V2). The calibrated model predicted patient-specific tumor status after five weeks of treatment (V3). Results: For a responder and non-responder, the differences between the observed and predicted percent change in tumor cellularity from V1 to V3 were less than 10%. Impact: Our biology-based mathematical model using quantitative MRI data has the potential to accurately predict tumor response to chemoradiation for patients with locally advanced cervical cancer.
Patel et al. (Tue,) studied this question.