Preoperative Application of Artificial Intelligence in Breast Cancer Surgery: A systematic review

Background and Aims: Breast cancer is the most frequent cancer and the second leading cause of cancer-related death among women all over the world. The surgical approach to treating breast cancer has undergone significant transformation over time. Artificial intelligence algorithms can refine surgical approaches and predict patient-specific outcomes. This review aims to investigate the potential preoperative applications of AI in breast cancer surgery. Methods: We conducted the current systematic review in accordance with the PRISMA guidelines. We conducted a systematic search of the PubMed, Web of Science, and Scopus databases from inception to February ۲۰۲۴. Original articles examining the preoperative application of AI, such as machine learning and deep learning in breast cancer surgery, were included. Results: The search yielded ۷۰۳ articles, of which ۳۹ studies were included in this systematic review. ۱۳ different types of AI applications were identified, including breast volume assessment, breast tumor segmentation, survival prediction, breast tissue classification, breast symmetry evaluation, tumor recurrence prediction, tumor dislocation prediction, pathological complete response (pCR) prediction, tumor margin prediction, tumor grading, neoadjuvant chemotherapy (NAC) response prediction, imaging analysis, and breast shape classification. Conclusion: Artificial intelligence is poised to play an increasingly important role in optimizing surgical care for breast cancer patients. By assisting with preoperative planning, intraoperative decision-making, and postoperative assessment, AI-based tools have the potential to improve oncologic outcomes, reduce surgical morbidity, and enhance the overall quality of breast cancer surgery. These technologies may become

کلیدواژه ها:

Breast cancer surgery ، Artificial intelligence ، Preoperative applications ، Machine learning ، Deep learning