A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chine
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ORIGINAL ARTICLE – CLINICAL ONCOLOGY
A prediction model to evaluate the pretest risk of malignancy in solitary pulmonary nodules: evidence from a large Chinese southwestern population Zuohong Wu1 · Tingting Huang1 · Shiqi Zhang1 · Deyun Cheng1 · Weimin Li1 · Bojiang Chen1 Received: 5 July 2020 / Accepted: 21 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose Lung cancer is the leading cause of cancer death and there have been clinical prediction models. This study aimed to evaluate the diagnostic performance of published models and create new models to evaluate the probability of malignant solitary pulmonary nodules (SPNs) in Chinese population. Methods We consecutively enrolled 2061 patients with SPNs from West China Hospital between January 2008 and December 2016, each SPN was pathologically confirmed. First, four published prediction models, Mayo clinic model, Veterans Affairs (VA) model, Brock model and People’s Hospital of Peking University (PEH) model were validated in our patients. Then, utilizing logistic regression, decision tree and random forest (RF), we developed three new models and internally validated them. Results Area under the receiver operating characteristic curve (AUC) values of four published models were as follows: Mayo 0.705 (95% CI 0.658–0.752, n = 726), VA 0.64 6 (95% CI 0.598–0.695, n = 800), Brock 0.575 (95% CI 0.502–0.648, n = 550) and PEH 0.675 (95% CI 0.627–0.723, n = 726). Logistic regression model, decision tree model and RF model were developed, AUC values of these models were 0.842 (95% CI 0.778–0.906), 0.734 (95% CI 0.647–0.821), 0.851 (95% CI 0.789–0.914), respectively. Conclusion The four published lung cancer prediction models do not apply to our population, and we have established new models that can be used to predict the probability of malignant SPNs. Keywords Lung cancer · Prediction model · Solitary pulmonary nodule · Chinese population Abbreviations AUC Area under the receiver operating characteristic curve CART Classification and regression tree CI Confidence interval CT Computer tomography HR Hazard ratio SPN Solitary pulmonary nodule PEH People’s Hospital of Peking University RF Random forest RRs Relative risks VA Veterans Affairs * Bojiang Chen [email protected] 1
Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, No. 37, Guo Xue Alley, Chengdu 610041, Sichuan, China
Background Nowadays, lung cancer remains the leading cause of cancer death worldwide (Al-Ameri et al. 2015). The wide use of low-dose computer tomography (CT) in cancer screening facilitates the early detection of patients with lung cancer and improves mortality among screened population (Bach et al. 2012; Criss et al. 2018; Henschke et al. 2006). Simultaneously, solitary pulmonary nodules (SPNs), as one of the radiological manifestations of lung cancer, are often detected. An SPN is defined as a single approximately round lesion surrounded by pulmonary parenchyma with a diameter less than 30 m
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