Evidence map›Paper›PMID 41684868›Full record

SynthesisSovremennye tekhnologii v meditsine2025

Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.

G V Danilov, S B Agrba, Yu V Strunina, A M Shevchenko, T A Konakova, S V Shugay, A I Batalov, I N Pronin

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Sovremennye tekhnologii v meditsine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

G V DanilovMD, PhD, Scientific Board Secretary, Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
S B AgrbaMD, PhD, Specialist, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
Yu V StruninaLeading Engineer, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
A M ShevchenkoRadiologist, Department of X-ray and Radioisotope Diagnostic Techniques; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
T A KonakovaRadiologist, Department of X-ray and Radioisotope Diagnostic Techniques; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
S V ShugayPathologist, Department of Pathology; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
A I BatalovMD, PhD, Radiologist, Department of X-ray and Radioisotope Diagnostic Techniques; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.
I N ProninMD, DSc, Professor, Academician of the Russian Academy of Sciences, Deputy Director; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glial tumors are the most common neuroepithelial neoplasms of the brain. Consequently, investigating robust, non-invasive techniques for subtyping these tumors - specifically through advanced multimodal neuroimaging and radiomics - is warranted. The present systematic review of scientific literature, including meta-analysis, was conducted to specify the major challenges of radiomics and machine learning in diagnostics of glial tumors based on the MRI data as well as to assess the quality of such non-invasive diagnostics. We analyzed 42 publications utilizing radiomics and machine learning to predict molecular biomarker status in glial tumors based on MRI data. The analysis covered mutations in the

Indexed as

Brain NeoplasmsGliomaMachine LearningMagnetic Resonance ImagingBiomarkers, TumorHumansRadiomicsBiomarkers, Tumorglial tumorsmachine learningmolecular biomarkersMR imaging of glial tumorsradiomics

Identifiers

PMID41684868
PMCPMC12892850

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.