ArticleFrontiers in artificial intelligence2026
BCMM: gated bidirectional cross-attention fusion of breast MRI and clinical features for predicting RCB response in the I-SPY1 cohort.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To develop a multimodal predictive framework that integrates breast magnetic resonance imaging (MRI) and structured clinical features for predicting residual cancer burden (RCB) following neoadjuvant therapy. Methods: The proposed Breast Cancer Multi-source Multi-scale Model (BCMM) was evaluated on the I-SPY1 cohort ( Results: Across the five folds, BCMM achieved a mean AUC of 0.826 ± 0.076 and a mean AUPRC of 0.926 ± 0.028. On pooled out-of-fold predictions, BCMM achieved an AUC of 0.795, an AUPRC of 0.895, an accuracy of 0.677, a balanced accuracy of 0.727, a macro-F1 score of 0.662, and an MCC of 0.406. Conclusion: BCMM showed promising internal cross-validated discrimination in this single-cohort study. Independent external validation is required before conclusions regarding robustness or clinical utility can be made.
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