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Methods: Plasma samples were profiled with liquid chromatography–tandem mass spectrometry (LC–MS/MS), and statistical analyses were performed by smoking status to construct risk models. Results: Among 1,723 participants (NSCLC and healthy controls), smoking-group models used eight significant metabolite indicators and nonsmoker models used eight different indicators (p \u003C 0.05), with AUC values reported for training and validation. Conclusions: The identified metabolites and model performance supported effective discrimination of NSCLC risk in both smoking and nonsmoking 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ARTICLE  \nDevelopment and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations  \nXu Zhang 1,2  | Cuicui Wang 1 | Chenwei Li 2 | Hui Zhao 1   \n1Department of Health Examination Center, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China  \n2Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China  \nCorrespondence  \nHui Zhao, Department of Health Examination Center, The Second Hospital of Dalian Medical University, 467 Zhongshan Rd, Shahekou District, Dalian, Liaoning, 116023, China.  \nEmail: [zhaohui@dmu.edu.cn](zhaohui@dmu.edu.cn)  \nFunding information  \nUnited Fund of the Second Hospital of Dalian Medical University and Dalian Institute of Chemical Physics of Chinese Academy of Sciences, Grant/Award Number: UF-ZD-202011; Liaoning Provincial Natural Science Foundation,  \nGrant/Award Number: 2023-MS-272  \nAbstract  \nBackground: Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non-small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation.  \nMethods: Plasma was collected from all patients enrolled for metabolic profiling by liquid chromatography–tandem mass spectrometry (LC–MS/MS) . The total population was divided into two groups according to smoking or not. Statistical analysis of metabolites was performed separately for each group and prediction models were constructed.  \nResults: A total of 1723 patients (1109 NSCLC patients and 614 healthy controls) were enrolled from the affiliated hospital during 2018 to 2021 . After grouping by smoking history, each group was statistically analyzed and prediction models were constructed, which resulted in eight indicators (propionylcarnitine, arginine, citrulline, etc.) significantly associated with lung cancer risk for smokers and eight indicators (dodecanoylcarnitine, hydroxybutyrylcarnitine, asparagine, etc.) for nonsmokers (p \u003C 0.05) . The smoker model indicated an AUC of 0.860 in the training set and 0.850 in the validation set. The nonsmoker model showed an AUC of 0.783 in the training set and 0 .762 in the validation set. Further calibration tests for both models indicated excellent goodness-of-fit results.  \nConclusions: In this study, we found a series of metabolites significantly associated with lung cancer incidence and constructed respectively prediction models for NSCLC risk in smokers and nonsmokers, with internal validation to confirm the efficiency to discriminate lung cancer risk in both smoking and nonsmoking states.  \nKEYW ORD S  \nmetabolite, nomogram, non-small cell lung cancer, prediction model  \nINTRODUCTION  \nLung cancer is one of the most common malignancies worldwide and the leading cause of cancer-related death, with more than 1 .7 million cases of deaths globally in 2020 .1 In China, the incidence and mortality rates of lung cancer are consistently the highest among all cancers.2 As there are often no specific clinical signs in the early stages of the  \ndisease process, most lung cancer patients are diagnosed in the middle to late stages, which has resulted in the 5-year survival rate of lung cancer patients in China still being less than 20% .3 Therefore, research into effective early screening strategies is an important part of improving the detection rate of lung cancer.  \nNumerous clinical studies and trials have demonstrated that the application of LDCT can effectively reduce lung  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2023 The Authors. Thoracic Cancer published by","cbCaivMEUHF5Pacr","https://ap.wps.com/l/cbCaivMEUHF5Pacr","pdf",2194333,13,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What was the study’s purpose regarding NSCLC risk prediction?\",\"answer\":\"To analyze plasma metabolic profiles and construct nomogram-based prediction models for NSCLC risk separately in smokers and nonsmokers, with internal validation.\"},{\"question\":\"How were plasma metabolites measured in the study?\",\"answer\":\"Plasma was analyzed using liquid chromatography–tandem mass spectrometry (LC–MS/MS) for metabolic profiling.\"},{\"question\":\"What performance metrics were reported for the prediction models?\",\"answer\":\"The study reported AUC values for both training and validation sets for smoker and nonsmoker models, along with calibration/goodness-of-fit results.\"}]","Development and internal validation of nomograms based on plasma metabolites to predict non-small cell lung cancer risk in smoking and nonsmoking populations | PDF",1790561932,33]