ArticleJournal of clinical laboratory analysis2022
A lung cancer risk prediction model for nonsmokers: A retrospective analysis of lung nodule cohorts in China.
Article in Journal of clinical laboratory analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.BioMed research international · 2025Pooled it
- Development of PET/CT-clinical nomograms for predicting lymph node metastasis in primary lung cancer.European radiology · 2026Article
- Diagnosis model of early malignant pulmonary nodules based on clinical laboratory data.Scientific reports · 2026Article
- Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China.BMC medicine · 2025Article
- Efficacy of three lung cancer prediction models in diagnosing benign and malignant pulmonary nodules.Translational cancer research · 2025Article
- A lung cancer risk prediction model for nonsmokers: A retrospective analysis of lung nodule cohorts in China.Journal of clinical laboratory analysis · 2022Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe risk of lung cancer in nonsmokers is increasing; however, there are relatively few studies on the risks of lung cancer in nonsmokers. PATIENTS AND
methodsWe collected epidemiological and clinical data from 429 nonsmoking patients with lung nodules from the Affiliated Li Huili Hospital as a training cohort and 123 nonsmoking patients with lung nodules as a testing cohort. We identified variables that might be related to malignant lung nodules from 27 variables by performing least absolute shrinkage and selection operator analysis. Univariate and multivariate analyses of these variables were conducted using binary logistic regression. Significant variables were used to generate a lung cancer risk prediction model for nodules in nonsmokers.
resultsWe successfully constructed a predictive nomogram incorporating density, ground-glass opacities, pulmonary nodule size, hypertension, plasma fibrinogen levels, and blood urea nitrogen. This model exhibited good discriminative ability, with a C-index value of 0.788 (95% confidence interval [CI]: 0.742-0.833) in the training cohort and 0.888 (95% CI: 0.835-0.941) in the testing cohort; it was well-calibrated in both cohorts. Decision curve analyses supported the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 18%. Ten-fold cross-validation indicated good stability and accuracy of the model (kappa = 0.416 ± 0.128; accuracy = 0.751 ± 0.056; area under the curve = 0.768 ± 0.049).
conclusionOur risk model can reasonably predict the risks of lung cancer in nonsmoking Chinese patients with lung nodules.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.