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March 18, 2026BMC Medical ImagingOpen Access

Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer

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Authors

PWPeiqi WangUniversity of California, Los AngelesHHHao HuZunyi Medical UniversityYWYubo WangKunming Medical University

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Overview

Research develops a predictive model for lymph node metastasis in lung cancer, suggesting effective clinical decision-making.

Key Points

  • The study aims to create and validate a predictive model for lymph node metastasis in lung cancer patients using radiomic features and clinical data.
  • Retrospective analysis of 403 lung cancer patients with pathologically confirmed diagnoses.
  • Radiomic features extracted from non-contrast computed tomography images using 3D Slicer.
  • Feature selection implemented using LASSO regression.
  • Multiple machine-learning models built to enhance predictive accuracy.
  • Model performance evaluated using AUC and decision curve analysis.
  • Lymph node metastasis was found in 35.5% of patients.
  • Identified 2 clinical features and 16 radiomic features strongly associated with LNM.
  • The combined clinical–radiomic support vector machine model had the best predictive performance: AUC of 0.927 in the training set, 0.852 in the internal test set, and 0.812 in the external test set.
  • Decision curve analysis indicated a high net clinical benefit for the combined model.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69ba430d4e9516ffd37a3e0bhttps://doi.org/10.1186/s12880-026-02262-x
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Also Consider

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  1. 1Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancer2025 · 38 citations
  2. 2Radiomics profiling combined with clinical risk factors for preoperative Lymphatic Metastasis prediction in Colorectal cancer: A multicenter study2026 · 1 citations
  3. 3PET-based radiomics as a preoperative predictor of pathologic lymph node metastasis in lung cancer.2026
  4. 4CT-Derived Radiomic Features for the Non-Invasive Differentiation of Mediastinal Lymphadenopathy in Lung Cancer and Sarcoidosis2026
  5. 5Development and validation of an MRI-based clinical-radiomics nomogram for predicting lymph node metastasis in non-small cell lung cancer2026