Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

作者：Xiuliang Guan, Yue Du, Rufei Ma, Nan Teng, Shu Ou, Hui Zhao, Xiaofeng Li · 发表于：BMC Medical Informatics and Decision Making · 年份：2023 · DOI：10.1186/s12911-023-02171-x · 被引用次数：82 · 研究领域：Metabolomics and Mass Spectrometry Studies、Ferroptosis and cancer prognosis、Lung Cancer Research Studies

BACKGROUND: Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches. RESULTS: In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer. CONCLUSIONS: This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict...