acePredicting lymph node metastasis in non-small cell lung cancer using clinical and PET/CT-derived morphological features: An interpretable machine-learning study
Background Accurate prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) is crucial for staging and treatment planning. This study aimed to establish a standardized machine learning workflow based on routinely available clinical, serological, and PET/CT-derived parameters to enhance preoperative LNM risk stratification. Methods This retrospective study included 255 patients with histologically confirmed NSCLC, all of whom underwent systematic lymph-node dissection. Patients were random…
A machine-learning model using four routine clinical and imaging predictors identified lymph node metastasis in NSCLC with moderate discrimination and a high negative predictive value in a held-out test cohort.
The same model had limited positive predictive value and did not significantly outperform a simple logistic regression reference, so it needs external multicenter validation before clinical use.
The study is retrospective with a small internal test cohort, the model showed limited positive predictive value, and the authors say external multicenter evaluation is required before clinical implementation.
Evidence
- Peer-reviewedPLOS One2026-10-15
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Truvace Impact Record TRV-2026-1979, v1: “Predicting lymph node metastasis in non-small cell lung cancer using clinical and PET/CT-derived morphological features: An interpretable machine-learning study.” Truvace, 2026-10-20. /record/TRV-2026-1979 (accessed at citation time). sha256 4c343d9705692b15…
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