Author:
Godlewski Adrian,Czajkowski Marcin,Mojsak Patrycja,Pienkowski Tomasz,Gosk Wioleta,Lyson Tomasz,Mariak Zenon,Reszec Joanna,Kondraciuk Marcin,Kaminski Karol,Kretowski Marek,Moniuszko Marcin,Kretowski Adam,Ciborowski Michal
Abstract
AbstractMetabolomics 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.
Funder
Subsidy of the Medical University of Bialystok
Voice Analysis for Medical Professionals
Grant financed by Polish National Science Centre
Excellence Initiative - Research University
Center for artificial intelligence at the Medical University of Bialystok
Publisher
Springer Science and Business Media LLC
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献