Development and validation of a prediction model for malignant pulmonary nodules: A cohort study. Issue 51 (23rd December 2021) - Ren, Zhen; Ding, Hongmei; Cai, Zhenzhen; Mu, Yuan; Wang, Lin; Pan, Shiyang
Abstract : Abstract: This study is to develop and validate a preoperative prediction model for malignancy of solitary pulmonary nodules. Data from 409 patients who underwent solitary pulmonary nodule resection at the First Affiliated Hospital of Nanjing Medical University, China between June 2018 and December 2020 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Clinical features, imaging parameters and laboratory data were then collected. Logistic regression analysis was used to develop a prediction model to identify variables significantly associated with malignant pulmonary nodules (MPNs) that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by concordance index and calibration plot, respectively. MPNs were confirmed in 215 (52.6%) patients by a pathological examination. Multivariate logistic regression analysis identified 6 risk factors independently associated with MPN: gender (female, odds ratio [OR] = 2.487; 95% confidence interval [CI]: 1.313–4.711; P = .005), location of nodule (upper lobe of lung, OR = 1.126; 95%CI: 1.054–1.204; P < .001), density of nodule (pure ground glass, OR = 4.899; 95%CI: 2.572–9.716; P < .001; part-solid nodules, OR = 6.096; 95%CI: 3.153–14.186; P < .001), nodule size (OR = 1.193; 95%CI: 1.107–1.290; P < .001), GAGE7 (OR = 1.954; 95%CI: 1.054–3.624; P = .033), and GBU4–5 (OR = 2.576; 95%CI: 1.380–4.806; P = .003). The concordance index was 0.86 (95%CI: 0.83–0.91) and 0.88 (95%CI: 0.84–0.94) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. We have developed and validated a preoperative prediction model for MPNs. The model could aid physicians in clinical treatment decision making.