AI Model Predicts Axillary Response Using Baseline Mammography

Neoadjuvant therapy, which involves systemic treatment administered before surgical resection, has become a standard of care for locally advanced breast cancer, offering the opportunity to downstage tumors and evaluate in vivo treatment response [1, 2]. While systemic regimens including dual human epidermal growth factor receptor 2 (HER2) blockade and immunotherapy have significantly improved pathological complete response rates, with some triple-negative protocols achieving an odds ratio of 3.95 (95% CrI 1.81 to 9.44) compared to standard chemotherapy [3], managing the axilla post-treatment remains a clinical challenge [4]. Current surgical standards often necessitate invasive axillary lymph node dissection for patients who were node-positive at diagnosis, despite the risk of long-term complications such as lymphedema and psychological distress, which can affect up to 78.5% of patients after neoadjuvant therapy [5]. Although sentinel lymph node biopsy, a procedure to identify and remove only the primary draining nodes, is an option for some, its pooled false-negative rate of 0.14 (95% CI 0.11 to 0.17) remains a concern in the post-neoadjuvant setting [6]. A new study now evaluates whether artificial intelligence can leverage baseline imaging to provide more precise guidance on axillary status.