Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer
Yuhan Liu,
Jinlin Ye,
Zecheng He,
Mingyue Wang,
Changjun Wang,
Jie Lang,
Yidong Zhou,
Wei Zhang
Affiliations
Yuhan Liu
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Jinlin Ye
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Zecheng He
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Mingyue Wang
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Changjun Wang
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Corresponding author
Jie Lang
Department of Breast Surgery, Beijing Longfu Hospital, Beijing, China; Corresponding author
Yidong Zhou
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Corresponding author
Wei Zhang
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China; Corresponding author
Summary: Precise lymph node evaluation is fundamental to optimize CDK4/6 inhibitor therapy in luminal breast cancer, particularly given contemporary trends toward axillary surgery de-escalation that may compromise traditional lymph node staging for recurrence risk evaluation. The lymph node prediction network (LNPN) was developed as a multi-modal model incorporating both clinicopathological parameters and ultrasonographic characteristics for lymph node burden differentiation. In a multicenter cohort of 411 patients, LNPN demonstrated robust performance, achieving an AUC of 0.92 for binary lymph node burden classification (N0 vs. N+) and 0.82 for ternary lymph node burden classification (N0/N1–3/N ≥ 4). Notably, among patients undergoing sentinel lymph node biopsy (SLNB) with confirmed 1–2 metastatic lymph nodes, LNPN predicted high-burden metastases (N ≥ 4) with an AUC of 0.77. LNPN provided a non-invasive method to assess lymph node metastasis and recurrence risk, potentially reducing unnecessary axillary lymph node dissection (ALND), and facilitating decision-making regarding the intervention of CDK4/6i in luminal breast cancer patients.
