Automated Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Carcinoma Using Deep Learning on Pretreatment Core Needle Biopsy Samples

Background/Aim: Early prediction of response to neoadjuvant chemotherapy (NAC) is essential for personalized treatment planning in breast carcinoma. Previous studies have relied on human-annotated regions of interest for digital pathology analysis rather than directly leveraging whole-slide images (WSIs). This study aimed to evaluate the predictive value of pretreatment core needle biopsy (CNB) WSIs for pathological complete response (pCR) to NAC using an artificial intelligence (AI)-based approach. Patients and Methods: We analyzed 130 patients with invasive ductal carcinoma who underwent anthracycline- or taxane-based NAC followed by surgery. From each pretreatment CNB WSI, five regions with the highest cellular density were selected to extract image patches. A fusion-based classification model was developed, integrating image data with clinical metadata, including age, hormone receptor status, and Ki-67 labeling index. Results: The model achieved an accuracy of 92.3%, comparable to those using expert annotations. Omitting either image or clinical data significantly reduced performance, underscoring their complementary roles. Optimal performance was achieved using five image patches of 1,000×1,000 pixels, balancing histological detail and computational efficiency. Conclusion: Our AI-based model accurately predicted pCR to NAC in breast carcinoma using only a limited number of high-cellularity image patches and basic clinical metadata, without requiring expert annotation. This approach may facilitate earlier treatment decisions and improve preoperative outcome prediction.