INTRODUCTION. Chemoradiation is the standard of care for locally advanced cervical cancer (LACC). However, patients still have a 20-30% risk of residual disease after completing therapy [1]. Optimizing chemoradiation to improve the therapy outcomes necessitates early and accurate predictions of tumor response on an individual basis, as population-based methods are limited in capturing tumor heterogeneity and longitudinal changes for individual patients [2]. Our goal is to accurately predict chemoradiation response for LACC patients using a biology-based mathematical model calibrated to magnetic resonance imaging (MRI) data [3]. METHODS. The patient cohort consisted of 10 LACC patients who received five weeks of concurrent cisplatin-based chemotherapy and external beam radiation [4]. Each patient underwent MRI consisting of T2-weighted, dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) sequences before (V1), after two weeks (V2), and after five weeks (V3) of concurrent chemoradiation [4]. To align the tumor regions of interest (ROIs) with the MR images, we applied rigid registrations within MRI exams and non-rigid registrations (with a rigid penalty on the tumor ROI) between exams [3]. A map of the number of tumor cells, NTC(x,t), where x is the 3D position and t is time, was calculated from the DW-MRI-derived apparent diffusion coefficient map using an established method [3]. We used a reaction-diffusion model that characterizes the change in NTC(x,t) as a function of tumor cell diffusion, proliferation, and response to treatment [3]. The effect of chemotherapy was modeled as an exponential decay in tumor cells, while cell death induced by radiation was modeled as an instantaneous reduction of the number of cells from NTC(x,t) to NTCpost(x,t) according to the linear-quadratic model [3, 5]. The proliferation and chemoradiation efficacy rates were calibrated between the NTC(x,t = V1) and NTC(x,t = V2) data using the Levenberg-Marquardt algorithm. The calibrated model was run forward to make a patient-specific prediction of NTC(x,t = V3). Prediction accuracy was evaluated by calculating the concordance correlation coefficients (CCCs) between the predicted and observed changes from V1 to V3 in total tumor cellularity and tumor volume. RESULTS AND CONCLUSION. For the study cohort, the CCC between the observed and predicted change was 0.95 for the total tumor cellularity and 0.83 for the tumor volume. These results indicate that our mathematical model—calibrated with patient-specific MRI data—can accurately predict the response of cervical cancer to chemoradiation. REFERENCES. [1] Touboul C et al. Oncologist. 2010. [2] Coveney PV et al. Philos Trans A Math Phys Eng Sci. 2016. [3] Jarrett AM et al. Nat Protoc. 2021. [4] Bowen SR et al. J Magn Reson Imaging. 2018. [5] Douglas BG et al. Radiat Res. 1976.