Predicting Neo-Adjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images

2025-05-20

AI Summary

This study addresses the clinical challenge of inaccurate prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC) patients. We propose an interpretable deep learning framework leveraging pre-treatment hematoxylin and eosin (H&E)-stained whole-slide images, built upon a ResNet architecture and validated via five-fold cross-validation. Attention-based visualization is integrated, and—novelty—we spatially co-localize model-identified high-weight regions with quantitative multiplex immunohistochemistry (mIHC) data. Results reveal significant enrichment of PD-L1, CD8⁺ T cells, and CD163⁺ macrophages within these high-weight regions, uncovering biologically meaningful immune microenvironment biomarkers. The model achieves 82% accuracy, an AUC of 0.86, and an F1-score of 0.84. This work establishes a new paradigm for personalized treatment decision-making in TNBC and mechanism-informed biomarker discovery.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, resulting in limited targeted treatment options. Neoadjuvant chemotherapy (NACT) is the standard treatment for early-stage TNBC, with pathologic complete response (pCR) serving as a key prognostic marker; however, only 40-50% of patients with TNBC achieve pCR. Accurate prediction of NACT response is crucial to optimize therapy, avoid ineffective treatments, and improve patient outcomes. In this study, we developed a deep learning model to predict NACT response using pre-treatment hematoxylin and eosin (H&E)-stained biopsy images. Our model achieved promising results in five-fold cross-validation (accuracy: 82%, AUC: 0.86, F1-score: 0.84, sensitivity: 0.85, specificity: 0.81, precision: 0.80). Analysis of model attention maps in conjunction with multiplexed immunohistochemistry (mIHC) data revealed that regions of high predictive importance consistently colocalized with tumor areas showing elevated PD-L1 expression, CD8+ T-cell infiltration, and CD163+ macrophage density - all established biomarkers of treatment response. Our findings indicate that incorporating IHC-derived immune profiling data could substantially improve model interpretability and predictive performance. Furthermore, this approach may accelerate the discovery of novel histopathological biomarkers for NACT and advance the development of personalized treatment strategies for TNBC patients.

Problem

• Predicting NACT response in TNBC using pre-treatment histopathologic images
• Improving therapy optimization by avoiding ineffective treatments
• Enhancing model interpretability with immune profiling data

Innovation

• Deep learning predicts NACT response from H&E images
• Model integrates mIHC data for immune profiling
• Attention maps reveal PD-L1 and immune cell biomarkers