Yun Li, Peng Wang, Xia Zou, Qing Zhang, Weibing Wang
The model achieved robust cross-center performance for luminal versus non-luminal classification. Combining SHAP with ablation revealed that a modality's attribution weight does not guarantee its irreplaceability, suggesting that both analyses are needed to evaluate modality contributions and guide efficient model design.
PURPOSE: To develop and externally validate a multimodal stacking fusion model based on dynamic contrast-enhanced MRI (DCE-MRI) for classifying luminal versus non-luminal breast cancer, and to assess each modality's contribution through interpretability and ablation analyses.
METHODS: We retrospectively enrolled 396 patients with pathologically confirmed invasive breast cancer from two centers, split into training (n = 220), validation (n = 56), and external test (n = 120) sets. Four unimodal models were trained independently on different feature sources: a clinical multilayer perceptron (Clinical-MLP), a whole-tumor radiomics model (Radiomics-ExtraTrees), a subregion-based habitat radiomics model (Habitat-ExtraTrees), and a transfer learning model (DL-ResNet50). Their predicted probabilities were fused via stacking, with the meta-classifier selected through a grid search over nine candidate model families that identified a support vector machine. Modality contributions were quantified by SHapley Additive exPlanations (SHAP) attribution and leave-one-modality-out ablation experiments.
RESULTS: The stacking model achieved AUCs of 0.906, 0.850, and 0.854 on the training, validation, and external test sets, It reached the highest AUC, with significant improvements over Clinical-MLP (P < 0.001) and DL-ResNet50 (P = 0.048), and a sensitivity of 0.839 for identifying luminal cases. SHAP ranked DL-ResNet50 as the dominant contributor (48.6%), and ablation confirmed that its removal caused the largest AUC decline (-0.033). Removing Radiomics marginally increased AUC (+0.003).
CONCLUSION: The model achieved robust cross-center performance for luminal versus non-luminal classification. Combining SHAP with ablation revealed that a modality's attribution weight does not guarantee its irreplaceability, suggesting that both analyses are needed to evaluate modality contributions and guide efficient model design.