Evidence map›Paper›PMID 42113388›Full record

ReviewRadiological physics and technology2026

Synthetic MRI and radiomics in multiple sclerosis: improving precision in diagnosis and prognosis.

Mohammad Mehdi Azizi, Korosh Saber, Rasool Azmoonfar, Daryoush Shahbazi-Gahrouei

Abstract readReview
PubMed Publisher
In one paragraph

Review in Radiological physics and technology, 2026. 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

4 authors.

Mohammad Mehdi AziziDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
Korosh SaberDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
Rasool AzmoonfarDepartment of Radiology, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
Daryoush Shahbazi-GahroueiDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran. shahbazi@med.mui.ac.ir.ORCID http://orcid.org/0000-0002-8538-1898

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammation, neurodegeneration, and gliosis in the central nervous system (CNS). While conventional magnetic resonance imaging (MRI) is essential for diagnosing and monitoring MS, it often fails to detect subtle microstructural changes. Synthetic MRI (SyMRI) produces multiple contrast-weighted images from a single acquisition and has emerged as a valuable tool for detailed tissue analysis. Combining SyMRI with radiomics, a method for extracting high-dimensional, sub-visual features from images, holds promise for enhancing MS assessment. This review critically evaluates the potential of this integration to improve lesion characterization, uncover early biomarkers of disease progression, and provide a more quantitative framework for monitoring treatment response, thereby moving towards personalized management strategies.

Indexed as

Image Processing, Computer-AssistedMagnetic Resonance ImagingMultiple SclerosisRadiomicsHumansPrognosisLesion detectionMultiple sclerosisQuantitative MRIRadiomicsSynthetic MRI

Identifiers

PMID42113388

What OpenQuestion holds

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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.