The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Simple Summary Breast cancer affects countless women worldwide, and detecting the spread of cancer to the lymph nodes is crucial for determining the best course of treatment. Traditional diagnostic methods have their drawbacks, but artificial intelligence techniques, such as machine learning and deep learning, offer the potential for more accurate and efficient detection. Researchers have developed cutting-edge deep learning models to classify breast cancer lymph node metastasis from medical images, with promising results. Combining radiological data and patient information can further improve the accuracy of these models. This review gathers information on the latest AI models for detecting breast cancer lymph node metastasis, discusses the best ways to validate them, and addresses potential challenges and limitations. Ultimately, these AI models could significantly improve cancer care, particularly in areas with limited medical resources. Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.