ArticleNature communications2020
Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
What it found
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Who cites it
100 citing papers in PubMed.
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Metabolomic and lifestyle profiles refine BMI-metabolic phenotypes in older adults.Cell reports. Medicine · 2026Observational
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Leveraging untargeted metabolomics in combination with machine learning to uncover novel insights into bladder cancer.Cancer & metabolism · 2026Article
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Fe metal-organic framework-derived heterojunction for metabolic diagnosis of thymic epithelial tumor.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Large-scale serum N-glycomics tracks N-glycosylation dynamics in hepatocellular carcinoma progression and enables early diagnosis.Nature communications · 2026Article
- Serum and urine metabolic fingerprints enable diagnosis and prognosis for IgA nephropathy.Materials today. Bio · 2025Article
- Multi-modal single-cell platform for nanoparticle-enhanced time-series metabolic profiles of CD8Journal of nanobiotechnology · 2025Article
- Serum metabolic profiling enables diagnosis, prognosis, and monitoring for brainstem gliomas.Nature communications · 2025Article
- Rapid and Noninvasive Early Detection of Lung Cancer by Integration of Machine Learning and Salivary Metabolic Fingerprints Using MS LOC Platform: A Large-Scale Multicenter Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Vessel-On-A-Chip Coupled Proteomics Reveal Pressure-Overload-Induced Vascular Remodeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Serum metabolomics to identify molecular subtypes and predict XELOX efficacy in colorectal cancer.Scientific reports · 2025Article
- Metabolomic insights into pathogenesis and therapeutic potential in adult acute lymphoblastic leukemia.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives.Cancer metastasis reviews · 2025Review
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
40 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.