A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

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.

Dietary factors and their influence on immunotherapy strategies in oncology: a comprehensive review

“…Leveraging genomic technologies, such as next-generation sequencing (NGS), along with other omics techniques like proteomics and metabolomics [ 178 – 180 ], will enable more precise tumor profiling and pave the way for personalized immunotherapies. Growing interest in machine learning models for biomarker research [ 181 ] adds another layer of potential.…”

Identifying Robust Biomarker Panels for Breast Cancer Screening

“…Other machine learning (ML) algorithms like Decision Trees, PCA, t-SNE and PLS have also shown promising results in identifying and classifying various cancers based on metabolomic data such as ovarian [23][24][25], lung [26][27][28], endometrial [29], skin and kidney carcinomas [30][31][32], glioma and meningioma brain tumors [33], and non-Hodgkin's lymphoma [34]. For breast cancer particularly, Henneges et al [35] achieved sensitivity and specificity of 83.5% and 90.6% respectively with an SVM-based metabolomic approach.…”

Terahertz Time-Domain Spectroscopy of Blood Serum for Differentiation of Glioblastoma and Traumatic Brain Injury

“…Various combinations of DRMs, ML, and feature selection methods form an ML pipeline [20]. The estimation of the efficiency of a created prediction data model is usually conducted using cross-validation methods.…”

“…GC-MS was used to measure the level of 2-hydroxyglutaric acid enantiomers in the blood serum, which are mutated isocitrate dehydrogenase proteins [16]. LC-MS combined with machine learning was used to study perturbations of the metabolic pathways of cell proliferation, regulation, survival, differentiation, and angiogenesis during glioma development [20]. NMR complements the methods mentioned above by studying the blood plasma and brain tissues [18,19].…”