Fig. 1

The overall design of the study. (a) Prediction of pCR after early-NAT in breast cancer can facilitate timely adjustment of therapy decision. (b) Participants in the ACRIN 6698 trial with useable MRI at both pre-NAT and early-NAT, were used to develop and internally test image-based models. An external test was conducted using a prospective cohort from a Chinese hospital. Clinicopathological characteristics were incorporated into the models for early pCR prediction. (c) The predictive model used a neural network-based quantitative analysis incorporating an enhanced self-attention module to capture dynamic information from longitudinal MRI before and after early-NAT. (d) The model’s performance was evaluated using feature importance ranking and ablation experiments, including single time-point images, incomplete sequences, and the removal of the enhanced self-attention module. ROC and DCA curves were compared for these experiments

Abbreviations: pCR = pathological complete response; NAT = neoadjuvant therapy; ROC = receiver operating characteristic; DCA = decision curve analysis