Fig. 2: Using the patient-specific digital twins to predict response to various therapeutic schedules.

The framework (a) consists of data preparation (i.e., image collection and processing pipeline), model personalization (i.e., calibration of the biology-based model using longitudinal MRI data), and response prediction. The prediction accuracy was evaluated by comparing the predicted final pathological status to the actual pathological status via ROC analysis (b). Applying both the actual schedule (navy curves in (c)) and the alternative schedule (red curves in (c)) of NAC to the digital twin allows for predicting the dynamics of tumor response (measured by the change of tumor volume over time; d) to each intervention. In this illustrative case, the actual therapeutic schedule leads to a predicted tumor volume after NAC (TV_T) larger than the pCR/non-pCR differentiating threshold (TV_T,J, determined from the optimal cutoff of ROC; see “Methods” section “Establishment of patient-specific digital twin to predict TNBC response to NAC” for details); thus, we predicted this patient as a non-pCR. In contrast, the alterative schedule leads to a predicted TV_T less than the TV_T,J; thus, we predicted the alternative schedule would lead to a pCR for this patient.