Speaker
Description
We present an image-driven computational model to enable patient-specific forecasting of triple-negative breast cancer (TNBC) response to neoadjuvant chemotherapy (NAC). Our approach integrates longitudinal magnetic resonance imaging (MRI) data with a biologically-based mechanistic model of tumor growth and therapy response.
Building upon our previously published model that couples drug pharmacokinetics/pharmacodynamic with a reaction-diffusion equation for tumor cell density, we introduce a key enhancement: a mechanical constraint on tumor cell proliferation, motivated by recent experimental evidence. The model's reduced parameter set, identified via global sensitivity analysis, is calibrated for each patient using Gauss-Newton iterations informed by early-treatment MRI scans. Forward simulations then generate spatiotemporal forecasts of tumor response for the remainder of the NAC regimen.
Global biomarkers are computed at regular intervals. The pipeline produces volumetric exports, time‑series summaries, and calibration histories to support reproducible evaluation of model fidelity and predictive performance in a unified imaging‑calibration-to‑forecast workflow. This work outlines our unified imaging-to-forecast-to-prediction workflow, which will be validated on a cohort of TNBC patients to assess its ability to forecast tumor dynamics and accurately identify pCR status.
