Radiomics and its application to neuro-oncology: A narrative review of advances, clinical application and implementation challenges
Synopsis
This narrative review searched PubMed, Scopus and Web of Science (2012-2024) to map the role of radiomics and artificial intelligence in neuro-oncology across diagnostic, prognostic and therapeutic potential, reporting that radiomics can automatically extract quantitative features from MRI, CT and PET/CT that are not visible to the human eye, has been shown useful for pre-surgical classification of gliomas, survival prediction and differentiation between tumour progression and pseudo-progression, may improve diagnostic accuracy and support patient stratification by molecular biomarkers such as IDH and MGMT when integrated with deep learning, and links image phenotypes to genetic alterations through radiogenomics to enhance personalised medicine, while limitations persist from lack of proto
Interpretation
Radiomics allows automated extraction of quantitative characteristics from medical images such as MRI, CT and PET/CT, identifying patterns not visible to the human eye. Relative to conventional visual image assessment, the review foregrounds automated quantitative characterisation of image information. A synthesis statement at the level of a narrative review, based on literature searched in PubMed, Scopus and Web of Science from 2012 to 2024.
Radiomics has been shown to be useful in pre-surgical classification of gliomas, survival prediction and differentiation between tumour progression and pseudo-progression. It anchors the value of radiomics in three concrete clinical problems in neuro-oncology rather than discussing image analysis in general terms. Conclusions aggregated from existing studies in the review; no specific sample sizes or effect sizes are given in the text.
Integration with deep learning algorithms may improve diagnostic accuracy and facilitate patient stratification based on molecular biomarkers such as IDH and MGMT; radiogenomics links image phenotypes with genetic alterations, enhancing personalised medicine. It connects radiomics with artificial intelligence, molecular biomarkers and genomic information, pointing toward personalised diagnostic and therapeutic pathways. A forward-looking integration prospect phrased with 'may' in the review, rather than confirmatory evidence.
Limitations persist due to the lack of standardisation of protocols, variability in segmentation, and the scarcity of validated multicentre studies. While affirming application prospects, it explicitly identifies the methodological conditions that hinder clinical translation. An aggregating judgement at the review level, without specific quantitative indicators.
Perspective
The review is aimed at researchers and clinical readers interested in neuro-oncological image analysis and personalised care, outlining application directions for radiomics in pre-surgical glioma classification, survival prediction, differentiation between tumour progression and pseudo-progression, and stratification based on molecular biomarkers such as IDH and MGMT, and pointing to radiogenomics as a path linking image phenotypes with genetic alterations; its conclusions are positioned as review-level synthesis, suited to understanding the field landscape and future research directions rather than directly guiding specific clinical decisions.
Readers would still watch how protocol standardisation and segmentation variability are resolved, whether multicentre prospective validation can accumulate sufficient evidence, how deep learning integration and molecular biomarker stratification perform in real clinical workflows, and how robust the radiogenomic link between image phenotypes and genetic alterations proves to be; moreover, the text available here is review-level abstract-style content without specific figures, sample sizes or statistical results, so the degree to which each application value is quantified remains an open question.