2D and 2.5D Deep Learning Models for Neoadjuvant Chemotherapy Response Prediction in Breast Cancer

Neoadjuvant chemotherapy (NAC) is central to managing locally advanced breast cancer, and accurate response assessment is essential for guiding clinical decision-making. This study developed and compared 2D and 2.5D deep learning models using multimodal breast Magnetic resonance imaging (MRI) to predict NAC response. A retrospective cohort of 187 patients (mean age 50.24 years) treated at two different hospitals between January 2020 − December 2024 was analyzed. Inclusion required histopathologically confirmed invasive breast cancer, completion of standardized NAC, pre- and post-treatment MRI (dynamic contrast-enhanced (DCE), T2WI, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC)), and postoperative pathological confirmation of response, including pathologic complete response (pCR). T1, T2, DWI, ADC, and multiphase DCE images were integrated into a seven-channel input, alongside a separate early-phase DCE input. Four convolutional neural networks (VGG16_bn, ResNet50, DenseNet121, and SqueezeNet1_0) were trained under 2D and 2.5D frameworks with transfer learning. Grad-CAM was applied for interpretability, and clinical features (speculation, edema changes) were incorporated into a combined predictive model. Performance evaluation included ROC, AUC(Area Under the Curve), sensitivity, calibration curves, and Hosmer–Lemeshow tests. In the training cohort, the fusion model achieved the highest performance (AUC 0.955). In the independent test cohort, 2.5D ResNet50 performed best (AUC 0.897, accuracy 0.895, sensitivity 0.944). Fusion further improved AUC to 0.955, demonstrating superior generalizability and clinical potential.