ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

2026-07-01

AI Summary

This study addresses key challenges in predicting pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer—namely, inadequate cross-modal modeling, high inter-center heterogeneity in imaging protocols, and limited interpretability—by integrating dynamic contrast-enhanced MRI (DCE-MRI), clinical variables, and pathological biomarkers. The authors construct intra-patient clinical prior graphs and propose a prior-guided relation-aware graph convolutional network for multimodal representation learning. To mitigate MRI protocol discrepancies and enhance cross-center robustness, a dual-branch domain adversarial strategy is introduced. Furthermore, they pioneer a large language model–driven subgraph retrieval-augmented generation (RAG) mechanism to fuse analogous case evidence and improve model interpretability. The proposed model achieves AUCs of 0.815 on an internal test set and 0.774 and 0.712 on two external test sets, demonstrating strong multicenter generalizability.

Abstract

Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment pathological complete response (pCR) prediction remains challenging due to insufficient cross-modal modeling, multicenter imaging heterogeneity, and weak evidence-grounded interpretability. We propose ClinRAG-GRAPH, a Clinically informed Retrieval-Augmented Generation Graph framework, for pre-treatment pCR prediction from DCE-MRI, structured clinical variables, and biopsy-derived pathological biomarkers. ClinRAG-GRAPH constructs an intra-patient clinical-prior graph and applies a prior-guided relation-aware graph convolutional network for structured multimodal representation learning. To improve cross-center robustness, we introduce a dual-branch domain-adversarial learning strategy to suppress protocol-related MRI bias while preserving pCR-relevant features. To enhance interpretability, we further incorporate large language model (LLM)-driven subgraph RAG module that retrieves clinically analogous historical cases and integrates retrieved evidence for pCR inference. We assemble a large-scale multicenter NAC breast cancer cohort for extensive validation, drawing from two public sources and three in-house centers.Results show that ClinRAG-GRAPH achieves AUCs of 0.815 on the internal test set and 0.774/0.712 on two external test sets, demonstrating robust pre-treatment pCR prediction across centers. The code is available at the anonymized https://github.com/miccai26-1181/ClinRAG-GRAPH.

Problem

• pCR prediction
• neoadjuvant chemotherapy
• multimodal modeling
• imaging heterogeneity
• interpretability

Innovation

• Retrieval-Augmented Generation
• Graph Neural Network
• Domain Adversarial Learning
• Multimodal Fusion
• Interpretable AI