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A nomogram for predicting the risk of invasive adenocarcinoma in patients with a sub-solid nodule
Received:April 16, 2021  Revised:June 10, 2021  Click here to download the full text
Citation of this paper:LIANG Qing,CHEN Shu-yan.A nomogram for predicting the risk of invasive adenocarcinoma in patients with a sub-solid nodule[J].Chinese Journal of Clinical Medicine,2021,28(5):879-884
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Author NameAffiliationE-mail
LIANG Qing Department of General Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China  
CHEN Shu-yan Department of Geriatrics, Xinhua Hospital Affiliated Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China shuyanchencn@163.com 
Abstract:Objective: To develop a model for predicting the risk of invasive pulmonary adenocarcinoma for sub-solid nodules, and provide a reference for the formulation of appropriate clinical diagnosis and treatment strategies for subsolid nodules. Methods: Totally, 221 patients with SSN who underwent surgical resection with a definite postoperative pathology from November 2019 to December 2020 in Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were included, patients' clinical data, serum tumor markers, and 17 parameters in radiological data were analyzed. The variables included in the model were screened by LASSO regression, then a risk evaluation nomogram model was established and verified. Results: Among the 221 patients with SSN, there were 2 atypical adenomatous hyperplasia (AAH), 44 adenocarcinomas in situ (AIS), 102 minimally invasive adenocarcinomas (MIA), and 73 invasive adenocarcinomas (IA). Five factors were selected by LASSO regression, including the maximum diameter of lung nodule, CT attenuation, shape, lobulation, and diameter of the solid component. The calibration curves for the probability of invasive pulmonary adenocarcinoma showed optimal agreement between the probability as predicted by the nomogram and the actual probability. Conclusions: The factors including the maximum diameter of lung nodule, CT attenuation, shape, lobulation, and diameter of the solid component might help to predict the risk of invasive pulmonary adenocarcinoma for sub-solid nodules and provide guidance for the subsequent personalized treatment.
keywords:sub-solid nodules  adenocarcinoma  predicting model  nomogram
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