A lung cancer risk prediction model for nonsmokers: A retrospective analysis of lung nodule cohorts in China. Issue 11 (1st November 2022)

• Record Type: Journal Article
• Authors: Liao, Zufang; Zheng, Rongjiong; Shao, Guofeng
• Is Part Of: Journal of clinical laboratory analysis. Volume 36:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-01
• Subjects: lung cancer -- model -- nonsmokers -- pulmonary nodules; Diagnosis, Laboratory -- Periodicals; Medical laboratory technology -- Periodicals; 616
• DOI: 10.1002/jcla.24748
• Languages: English
• ISSNs: 0887-8013

Abstract

Abstract: Background: The risk of lung cancer in nonsmokers is increasing; however, there are relatively few studies on the risks of lung cancer in nonsmokers. Patients and Methods: We 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. Results: We 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). Conclusion: Our risk model can reasonably predict the risks of lung cancer in nonsmoking Chinese patients with lung nodules. Abstract : We deeply researched the effects on different lung nodules of individual variables at different levels through univariate, multivariate, binary, and multinomial logistic regression analyses . The model was constructed on the bias of those significant variables. This nomogram could help predict the risks of lung cancer in nonsmoking patients with lung nodules.