ArticleCancer biology & medicine2026
Multimodal artificial intelligence predicts PIK3CA mutation in breast cancer from digital pathology and clinical data: a multicenter study.
Article in Cancer biology & medicine, 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
objectiveAccurate detection of
methodsA total of 1,047 patients from TCGA and 166 patients from 3 external centers were included. The histopathology model used a transformer-based pretrained encoder (H-optimus-0) and a clustering-constrained attention multiple instance learning (CLAM-SB MIL) classifier to generate WSI-level representations. The clinical model incorporated engineered clinical variables and an extreme gradient boosting (XGBoost) model. A decision-level late fusion strategy (Multimodal
resultsMPM outperformed single-modality models. It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts (0.695, 0.690, and 0.680). SHAP analysis identified molecular subtype as the most influential clinical feature, whereas attention maps highlighted mutation-associated morphological regions.
conclusionsThe developed multimodal framework effectively integrates complementary morphological and clinical information, and provides a robust and generalizable method for predicting
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