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

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.

@article{khan2025_2505.14730,
  title={ Predicting Neo-Adjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images },
  author={ Hikmat Khan and Ziyu Su and Huina Zhang and Yihong Wang and Bohan Ning and Shi Wei and Hua Guo and Zaibo Li and Muhammad Khalid Khan Niazi },
  journal={arXiv preprint arXiv:2505.14730},
  year={ 2025 }
}