ArticleScientific reports2023
A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Liquid Biopsy in Primary CNS Tumors: Bridging Biology, Technology, and Clinical Care.Molecular diagnosis & therapy · 2026Review
- ML-BUSMetab: Machine Learning-Based Metabolomic Profiling for Predicting Aspirin Response in Colorectal Cancer Chemoprevention: A Multi-Model Explainable Artificial Intelligence Approach with External Validation.Journal of clinical medicine · 2026Article
- Metabolic Landscape and Emerging Therapeutic Potential in Pediatric and Adult Gliomas.International journal of molecular sciences · 2026Review
- FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI.Scientific reports · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Towards non-invasive diagnosis of glioblastoma: identifying metabolic biomarkers in liquid biopsies using a ROC-based approach.Discover oncology · 2025Article
- Preclinical atherosclerosis and prediabetes: a cross-sectional metabolic assessment in apparently healthy population.Cardiovascular diabetology · 2025Article
- Exploring the use of exhaled breath profiling for non-invasive monitoring of cognitive functioning in children: a pilot study.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Leveraging glycosylation for early detection and therapeutic target discovery in pancreatic cancer.Cell death & disease · 2025Review
- Metabolomic profiling of plasma from glioma and meningioma patients based on two complementary mass spectrometry techniques.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Enhanced Detection of Gastrointestinal Malignancies using Machine Learning-Optimized Liquid Biopsy: A Mini Review.Current cancer drug targets · 2025Review
- Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.Cell communication and signaling : CCS · 2024Article
- Dietary factors and their influence on immunotherapy strategies in oncology: a comprehensive review.Cell death & disease · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.