ArticleThoracic cancer2025
Image-Based Deep Learning Model for Predicting Lymph Node Metastasis in Lung Adenocarcinoma With CT ≤ 2 cm.
Article in Thoracic cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- Preoperative colon cancer nodal staging using dual-energy CT and clinically derived features.European radiology · 2026Article
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7 authors.
Funding
Abstract
backgroundLymph node metastasis (LNM) poses a considerable threat to survival in lung adenocarcinoma. Currently, minor resection is the recommended surgical approach for small-diameter lung cancer. The accurate preoperative identification of LNM in patients with small-diameter lung cancer is important for improving patient survival and outcomes.
methodsA total of 1740 patients with clinical early-stage lung adenocarcinoma who underwent surgical resection were enrolled in this study. The Lasso model was used to screen clinical and imaging features, and multivariate logistic regression analysis was used to analyze the relevant diagnostic factors to establish a diagnostic model for predicting LNM. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curve analysis were used to verify the clinical efficacy of the model, which was further validated with an internal validation set.
resultsThe proportion of solid components (PSC), sphericity, nodule margin, entropy, and edge blur were identified as diagnostic factors that were strongly correlated with LNM in lung adenocarcinoma patients. The area under the ROC curve (AUC) in the internal training set was 0.91. Decision curve analysis revealed that the model could achieve greater benefits for patients. The calibration curve was used to further verify the applicability of the prediction model.
conclusionsPatients with early-stage lung adenocarcinoma with LNM can be identified by typical imaging features. The diagnostic model can help to optimize surgical planning among thoracic surgeons.
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