Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules

• DOI: 10.3389/fonc.2024.1334504
• Publication Date: 2024-07-01T04:49:55Z
• Authors: Mengchao Xue; Rongyang Li; Junjie Liu; Ming Lu; Zhenyi Li; Huiying Zhang; Hui Tian
• Keywords: logical model; 03 medical and health sciences; 0302 clinical medicine; solitary pulmonary nodules; invasive pulmonary adenocarcinoma; Oncology; diagnosis; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; prediction; RC254-282

BackgroundThis study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs).MethodWe analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA).ResultThe final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817–0.871) and 0.812 (95% CI, 0.766–0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively.ConclusionOur predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.