ArticleBMC medical informatics and decision making2023
Construction of the XGBoost model for early lung cancer prediction based on metabolic indices.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review.Clinical and experimental dental research · 2025Pooled it
- [Research progress of metabolomics in the pathogenesis and treatment of constipation in children].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.Journal of translational medicine · 2026Article
- Gene-based lung cancer detection system through omix data and optimized convolutional neural network.Journal of computer-aided molecular design · 2026Article
- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.Schizophrenia bulletin · 2026Article
- Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features.BMC medical informatics and decision making · 2026Article
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- Development and validation of a nomogram using interpretable machine learning to integrate CT radiomics and PET metabolic parameters for predicting benign-malignant differentiation of pulmonary space-occupying lesions.Frontiers in radiology · 2026Article
- Osteoporotic fractures prediction in Chinese postmenopausal women: a machine learning-based multi-dimensional approach.Frontiers in endocrinology · 2026Article
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Integrated machine learning and causal inference reveal CHRDL1 as a diagnostic and functional biomarker in NSCLC.Discover oncology · 2025Article
- Article
- Metabolite biomarkers in lung cancer: unlocking the potential of body fluid analysis for early detection and prognosis-a narrative review.Translational lung cancer research · 2025Review
- Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants.BMJ paediatrics open · 2025Article
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- The Future of Tumor Markers: Advancing Early Malignancy Detection Through Omics Technologies, Continuous Monitoring, and Personalized Reference Intervals.Biomolecules · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung 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.
resultsIn 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.
conclusionsThis study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.
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