Liu Yuhan, Ye Jinlin, He Zecheng, Wang Mingyue, Wang Changjun, Lang Jie, Zhou Yidong, Zhang Wei
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.
iScience. 2025 Jun 7;28(7):112849. doi: 10.1016/j.isci.2025.112849. eCollection 2025 Jul 18.
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/ ≥ 4). Notably, among patients undergoing sentinel lymph node biopsy (SLNB) with confirmed 1-2 metastatic lymph nodes, LNPN predicted high-burden metastases ( ≥ 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.
精确的淋巴结评估对于优化管腔型乳腺癌的CDK4/6抑制剂治疗至关重要，特别是考虑到当前腋窝手术降级的趋势，这可能会影响用于复发风险评估的传统淋巴结分期。淋巴结预测网络（LNPN）是作为一种多模态模型开发的，它结合了临床病理参数和超声特征来区分淋巴结负荷。在一个由411名患者组成的多中心队列中，LNPN表现出强大的性能，二元淋巴结负荷分类（N0 vs. N+）的AUC为0.92，三元淋巴结负荷分类（N0/N1-3/≥4）的AUC为0.82。值得注意的是，在接受前哨淋巴结活检（SLNB）且确诊有1-2个转移性淋巴结的患者中，LNPN预测高负荷转移（≥4个）的AUC为0.77。LNPN提供了一种评估淋巴结转移和复发风险的非侵入性方法，有可能减少不必要的腋窝淋巴结清扫（ALND），并有助于管腔型乳腺癌患者CDK4/6i干预的决策制定。
