ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
MRI radiomics reveals pain-related biomarkers for modic changes.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundModic changes (MC) are vertebral endplate abnormalities detectable on MRI, strongly linked to low back pain (LBP). Identifying imaging features correlated with pain severity may improve diagnosis and guide treatment. This study aims to develop an automated radiomics-based model for classifying MC and investigating its association with LBP severity.
methodsA total of 304 retrospective and 101 prospective patients with MRI-confirmed MC (January 2020–May 2024) were analyzed. Radiomic features were extracted from T1, T2, and FS sequences using Pyradiomics, yielding 1,906 features. The Mann-Whitney U-test and LASSO algorithm identified 29 key features. A Naive Bayes model was trained (70%) and validated (30%) using retrospective data. Prospective patient data, including Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI) scores, were analyzed for correlations with pain severity.
resultsThe model achieved 99% accuracy in classifying Modic types. Seven radiomic features showed significant associations with pain severity. Higher VAS scores correlated positively with short-run high gray-level emphasis and size-zone non-uniformity normalized, and negatively with low gray-level zone emphasis (all P < 0.05). ODI scores showed negative associations with 10th-percentile intensity, elongation, and median intensity, and a positive association with skewness (all P < 0.05). Additionally, focused volumetric analysis (mesh volume, voxel volume, surface area) revealed no linear or nonlinear associations with pain (all P > 0.05), indicating that texture-based inflammatory signatures rather than lesion size may better characterize Modic-related pain.
conclusionsThe radiomics-based Naive Bayes model accurately classifies Modic changes and identifies imaging features linked to LBP severity. These findings support improved diagnostics, early intervention, and personalized rehabilitation for MC-related spinal disorders.
Indexed as
Identifiers
41249666What OpenQuestion holds
Registered trials
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.