Abstract Purpose: We seek to establish the generalizability of our biology-based mathematical model in accurately predicting the response of locally advanced breast cancer (LABC) patients to neoadjuvant therapy (NAT). Patients and Methods: 91 patients (representing three subtypes of LABC) from 10 I-SPY 2 clinical trial sites received quantitative MRI before (V1), three weeks into (V2), and after completion of (V3) the first 12-week standard-of-care or experimental NAT course. We used these data to calibrate, on a patient-specific basis, our previously developed biology-based mathematical model describing the spatiotemporal change in the number of tumor cells. After calibrating the mathematical model to the V1 and V2 MRI data, the calibrated model predicted the patient-specific tumor status at V3 by explicitly accounting for tumor cell movement (constrained by the mechanical properties of the surrounding tissue), proliferation, and death due to treatment. Results: The concordance correlation coefficient between the observed and predicted tumor change from V1 to V3 was 0.94 for total cellularity and 0.91 for volume. A logistic regression model of predicted tumor volume metrics from V1 to V3 differentiated pCR from non-pCR patients with an area under the receiver operating characteristic curve of 0.78. Conclusions: Our tumor forecasting pipeline can accurately predict tumor status after an NAT course—on a patient-specific basis, without a training dataset—using “real-world” MRI data obtained from a multi-subtype, multi-site clinical trial.
Patel et al. (2025) studied this question.