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Radiomics and Molecular Biology in Glioma: A Synergistic Approach

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Dr. Anna Luisa Di Stefano

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Guest Editor

Division of Neurosurgery, Spedali Riuniti di Livorno—USL Toscana Nord-Ovest, 57124 Livorno, Italy
Interests: brain tumors; glioma; neurobiology; neuro-radiomic; brain tumors treatment

Dr. Gianluca Grimod

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Guest Editor

Division of Neurosurgery, Policlinico San Matteo Pavia, Viale Camillo Golgi, 19, 27100 Pavia, Italy
Interests: neurosurgery; brain tumors; neurobiology; neuro radiomic

Special Issue Information

Dear Colleagues,

Glioma, a malignant brain tumor, presents significant challenges in diagnosis, prognosis, and treatment. Radiomics, a quantitative image analysis technique, extracts a wealth of information from medical images, providing insights into tumor biology and response to therapy. Molecular biology, on the other hand, focuses on understanding the genetic and molecular alterations underlying tumorigenesis. By integrating radiomics and molecular biology, researchers aim to develop more accurate prognostic biomarkers, optimize treatment strategies, and ultimately improve patient outcomes. This Special Issue will delve into the current state of the art in radiomics and molecular biology for glioma, exploring their synergistic potential in advancing personalized medicine.

Dr. Anna Luisa Di Stefano
Dr. Gianluca Grimod
Guest Editors

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30 pages, 1988 KB

Open AccessEditor’s ChoiceSystematic Review

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

by Edoardo Agosti, Karen Mapelli, Gianluca Grimod, Amedeo Piazza, Marco Maria Fontanella and Pier Paolo Panciani

Cancers 2026, 18(3), 491; https://doi.org/10.3390/cancers18030491 - 2 Feb 2026

Cited by 3 | Viewed by 1046

Abstract

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

(This article belongs to the Special Issue Radiomics and Molecular Biology in Glioma: A Synergistic Approach)

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