ArticleMetabolomics : Official journal of the Metabolomic Society2022
Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.
Article in Metabolomics : Official journal of the Metabolomic Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Evolving role of biomarkers beyond prostate‑specific antigen in refining prostate cancer screening: From blood‑based tests to urinary and genetic markers (Review).Molecular medicine reports · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Cross-cancer survival prediction using machine learning models.Scientific reports · 2026Article
- Non-invasive hair metabolome analysis reveals GERD and thyroid dysfunction in pulmonary fibrosis patients.Metabolism open · 2025Article
- [Applications and Advances of Metabolomics in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
- Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer.NPJ precision oncology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets.Cancers · 2025Article
- Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.Biomolecules · 2025Review
- Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.Scientific reports · 2024Article
- [Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Research in the application of artificial intelligence to lung cancer diagnosis.Frontiers in medicine · 2024Review
- Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma.NPJ precision oncology · 2023Article
- JI017 Induces Cell Autophagy and Apoptosis via Elevated Levels of Reactive Oxygen Species in Human Lung Cancer Cells.International journal of molecular sciences · 2023Article
- Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling.Annals of biomedical engineering · 2023Article
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3 authors.
Funding
Abstract
introductionWhile prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.
objectivesImplement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.
methodsData were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.
resultsOverall survival (OS) is predicted in external validation cohort with AUROC
conclusionThis study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.
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