aceKnowledge-guided Multimodal Learning for Interpretable Prediction of Pathological Complete Response and Survival in Breast Cancer
Purpose Pretreatment estimation of pathological complete response (pCR) to neoadjuvant chemotherapy may support risk stratification and future response-adapted clinical studies in breast cancer. However, existing deep learning approaches often rely on "black-box" visual pattern recognition, suffering from poor interpretability, high data dependency, and limited generalizability across heterogeneous cohorts. Here, we present CLASP (Clinical Language-Aligned Slide Pathology), a knowledge-driven, multimodal framewo…
A model combining slide images, clinical data and language-model-generated pathology knowledge predicted complete response with AUC 0.845 to 0.881 across internal and external cohorts.
The authors present the findings only as suggestive and describe the framework as a candidate for pretreatment risk stratification, not a proven clinical tool.
Evidence
- Peer-reviewedLaboratory Investigation2026-10-09
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Truvace Impact Record TRV-2026-1658, v1: “Knowledge-guided Multimodal Learning for Interpretable Prediction of Pathological Complete Response and Survival in Breast Cancer.” Truvace, 2026-10-17. /record/TRV-2026-1658 (accessed at citation time). sha256 8765a52d3728981b…
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