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MRI-Based Radiomics for Non-Invasive Prediction of Molecular Biomarkers in Gliomas.

Edoardo Agosti1, Karen Mapelli1, Gianluca Grimod2

  • 1Division of Neurosurgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy.

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|February 13, 2026
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Summary

Radiomics shows promise for predicting glioma molecular biomarkers non-invasively using MRI data. However, clinical use is limited by inconsistent methods and lack of standardization.

Keywords:
deep learninggliomamachine learningmagnetic resonance imagingmolecular biologyradiomicssystematic review

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Area of Science:

  • Radiomics and medical imaging analysis
  • Neuro-oncology and precision medicine
  • Machine learning in healthcare

Background:

  • Radiomics offers non-invasive characterization of glioma molecular landscape using quantitative MRI data.
  • Supports precision oncology by predicting genomic, epigenetic, and phenotypic alterations without invasive sampling.
  • Systematic review synthesizes radiomics applications for non-invasive glioma biomarker prediction.

Purpose of the Study:

  • To systematically review radiomics applications for non-invasive prediction of molecular biomarkers in gliomas.
  • To evaluate current methodological trends, performance metrics, and translational readiness of radiomics in glioma research.

Main Methods:

  • Systematic literature search following PRISMA 2020 guidelines across PubMed, Ovid MEDLINE, and Scopus.
  • Data extraction from 70 eligible studies (10,324 patients) on MRI sequences, segmentation, computational methods, and biomarkers.
  • Methodological quality assessed using Radiomics Quality Score (RQS), IBSI criteria, and Newcastle-Ottawa Scale (NOS).

Main Results:

  • IDH mutation was the most predicted biomarker (70% of studies), followed by ATRX (38.6%).
  • High diagnostic performance reported (AUCs 0.71-0.99), with deep learning/hybrid models showing superior results.
  • Significant heterogeneity in imaging protocols, segmentation, and reporting, with inconsistent adherence to standardization criteria.

Conclusions:

  • Radiomics holds strong potential for non-invasive glioma molecular biomarker prediction with high diagnostic accuracy.
  • Clinical translation is impeded by heterogeneous protocols, lack of standardization, insufficient validation, and variable methodological rigor.