Automated Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Carcinoma Using Deep...

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.

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.

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.

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.