ArticleAmerican journal of cancer research2024
Risk factors for mediastinal lymph node metastases in early-stage non-small-cell lung cancer and prediction model establishment.
Article in American journal of cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- Development of PET/CT-clinical nomograms for predicting lymph node metastasis in primary lung cancer.European radiology · 2026Article
- Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer.BMC medical imaging · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to explore the risk factors for mediastinal lymph node metastases (MLNM) in patients with early-stage non-small-cell lung cancer (NSCLC) and to establish a predictive model. A retrospective analysis was conducted on the clinical data from NSCLC patients treated at the Second Affiliated Hospital of Guangzhou Medical University and the First Affiliated Dongguan Hospital of Guangdong Medical University between March 2021 and March 2023. Baseline clinical data, laboratory parameters, and pathological features were collected and analyzed. Univariate and multivariate logistic regression identified several independent risk factors for MLNM, including Cyfra21-1, D-dimer (D-D), tumor size, percentage of tumor solid, and lesion location. These risk factors were incorporated into a Nomogram model to visually assess the likelihood of MLNM. The model demonstrated excellent diagnostic accuracy with an area under the curve (AUC) of 0.904, a specificity of 73.85%, and a sensitivity of 93.68%. Cyfra21-1 and D-D were particularly significant predictors of MLNM. This Nomogram model provides an effective and practical tool for assessing MLNM risk in early-stage NSCLC, aiding clinical decision-making and optimizing treatment strategies.
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