ArticleiScience2025
Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
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/
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