• Zhanyue Pang, Haibo Liu, Zhitao Chen, and Liangming Zhu.
• Jinan Central Hospital, Shandong University, Jinan, Shandong, China.
• J Coll Physicians Surg Pak. 2022 Dec 1; 32 (12): 156315691563-1569.

ObjectiveTo establish and verify a nomogram for predicting distant metastasis in invasive lung adenocarcinoma (IAC).Study DesignObservational study.Place And Duration Of StudyDepartment of Thoracic Surgery, Jinan Central Hospital, Jinan, China, from December 2021 to May 2022.MethodologyTo create a nomogram, univariate and multivariate logistic regression analyses were used to identify the independent predictors of distant metastasis. The calibration, discrimination, and clinical performance of the nomogram were tested by calibration plots, area under receiver operating characteristic curve (AUC), and decision curve analysis (DCA).ResultsAge at diagnosis (<70 years), histological type (invasive mucinous adenocarcinoma), T stage, N stage, surgical approach, and lymph node dissection were independent predictors for the development of nomogram. Compared with the American Joint Committee on Cancer-8th edition staging system, AUC showed that this prediction model has a higher predictive performance (training set: 0.922 vs. 0.790; verification set: 0.919 vs. 0.779). In addition, the overall survival time (OS) of IAC patients was meaningfully different among the three groups of different risks stratified based on model score (p <0.001).ConclusionThe prediction model constructed according to factors such as histological type and surgical approach in this study can accurately predict distant metastasis in IAC patients and define high-risk patients according to nomogram score.Key WordsInvasive adenocarcinoma IAC, Distant metastasis, Nomogram, Surveillance, Epidemiology and end results SEER.

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