Correlation Study between Neoadjuvant Chemotherapy Response and Long-term Prognosis in Breast Cancer Based on Deep Learning Models

Background: Neoadjuvant chemotherapy (NAC) is a critical component of breast cancer treatment; however, patient responses and long-term prognoses vary significantly. Accurately predicting post-NAC prognosis is essential for guiding individualized treatment plans. This study aims to develop a deep learning-based prediction model to analyze the correlation between NAC efficacy and long-term outcomes in breast cancer patients, providing a new approach to identifying high-risk populations. Objective: To construct a deep learning model that integrates multi-dimensional clinical and pathological parameters to predict recurrence and metastasis risk in breast cancer patients following NAC, thereby facilitating personalized treatment strategies. Methods: A retrospective analysis was conducted on 832 breast cancer patients who received NAC at our hospital from 2013 to 2022. Comprehensive clinical, pathological, and molecular subtype data including：pre- and post-NAC tumor characteristics, Ki-67 index, lymph node status, lymphovascular invasion, and Miller-Payne grading were collected. A Multi-layer Perceptron (MLP) based deep learning model was developed, incorporating ensemble learning strategies to integrate multi-modal prediction results. The model’s performance in assessing recurrence and metastasis risk was evaluated across different breast cancer subtypes. Results: The analysis identified key prognostic factors, including tumor size reduction, post-NAC lymph node status, Ki-67 index, lymphovascular invasion, and Miller-Payne grading. The MLP model achieved AUC values of 0.86 (95% CI: 0.82-0.93) for HER2+,0.82 (95% CI: 0.70-0.92)for triple-negative breast cancer, and 0.76 (95% CI: 0.66-0.82) for HR+/HER2−. The model successfully stratified high-risk subgroups with significant differences in prognosis, providing valuable insights for clinical decision-making. Conclusions: The deep learning-based prediction model developed in this study effectively assesses the prognostic risk of breast cancer patients after NAC. Its clinical application holds potential for optimizing individualized treatment and follow-up strategies, ultimately improving patient outcomes.