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Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction
by
, , , and
Olga Vershinina
1,2,*,
Victoria Turubanova
1,2,3,
Mikhail Krivonosov
1,2,
Arseniy Trukhanov
4 and
Mikhail Ivanchenko
1,2 1
Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia
2
Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia
3
Department of Genetics and Life Sciences, Sirius University, Sochi 354340, Russia
4
Mriya Life Institute, National Academy of Active Longevity, Moscow 124489, Russia
*
Author to whom correspondence should be addressed.
Cancers 2025, 17(16), 2614; https://doi.org/10.3390/cancers17162614 (registering DOI)
Submission received: 24 June 2025
/
Revised: 25 July 2025
/
Accepted: 6 August 2025
/
Published: 9 August 2025
(This article belongs to the Section Methods and Technologies Development)
Simple Summary
Distinguishing glioma subtypes and assessing patient survival is a non-trivial task due to the high heterogeneity of these brain tumors. Accurate diagnosis is a critical step in developing treatment tactics. In this study, using publicly available RNA sequencing data, we identified a set of key genes and built explainable AI models to classify the major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predict patient survival. Experiments evaluating the models demonstrated their ability to generate highly accurate predictions. At the same time, the explainable artificial intelligence approach allowed us to identify relationships between the expression levels of the selected genes and the predictions of the models. Taken together, the obtained results indicate the potential of our predictive models for glioma diagnosis.
Abstract
Background/Objectives: Gliomas are complex and heterogeneous brain tumors characterized by an unfavorable clinical course and a fatal prognosis, which can be improved by an early determination of tumor kind. Here, we developed explainable machine learning (ML) models for classifying three major glioma subtypes (astrocytoma, oligodendroglioma, and glioblastoma) and predicting survival rates based on RNA-seq data. Methods: We analyzed publicly available datasets and applied feature selection techniques to identify key biomarkers. Using various ML models, we performed classification and survival analysis to develop robust predictive models. The best-performing models were then interpreted using Shapley additive explanations (SHAP). Results: Thirteen key genes (TERT, NOX4, MMP9, TRIM67, ZDHHC18, HDAC1, TUBB6, ADM, NOG, CHEK2, KCNJ11, KCNIP2, and VEGFA) proved to be closely associated with glioma subtypes as well as survival. Support Vector Machine (SVM) turned out to be the optimal classification model with the balanced accuracy of 0.816 and the area under the receiver operating characteristic curve (AUC) of 0.896 for the test datasets. The Case-Control Cox regression model (CoxCC) proved best for predicting survival with the Harrell’s C-index of 0.809 and 0.8 for the test datasets. Using SHAP we revealed the gene expression influence on the outputs of both models, thus enhancing the transparency of the prediction generation process. Conclusions: The results indicated that the developed models could serve as a valuable practical tool for clinicians, assisting them in diagnosing and determining optimal treatment strategies for patients with glioma.
