Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer
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 predicti...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225011101 |
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| Summary: | 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. |
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| ISSN: | 2589-0042 |