ArticleEuropean radiology2025
Exploring factors driving the evolution of chronic lesions in multiple sclerosis using machine learning.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Longitudinal changes of choroid plexus volumes and MRI ratios in multiple sclerosis.Brain communications · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesThe study aimed to identify factors influencing the evolution of chronic lesions in multiple sclerosis (MS) using a machine learning approach. MATERIALS AND
methodsLongitudinal data were collected from individuals with relapsing-remitting multiple sclerosis (RRMS). The "iron rim" sign was identified using quantitative susceptibility mapping (QSM), and microstructural damage was quantified via T1/fluid attenuated inversion recovery (FLAIR) ratios. Additional data included baseline lesion volume, cerebral T2-hyperintense lesion volume, iron rim lesion volume, the proportion of iron rim lesion volume, gender, age, disease duration (DD), disability and cognitive scores, use of disease-modifying therapy, and follow-up intervals. These features were integrated into machine learning models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) to predict lesion volume change, with the most predictive model selected for feature importance analysis.
resultsThe study included 47 RRMS individuals (mean age, 30.6 ± 8.0 years [standard deviation], 6 males) and 833 chronic lesions. Machine learning model development results showed that the SVM model demonstrated superior predictive efficiency, with an AUC of 0.90 in the training set and 0.81 in the testing set. Feature importance analysis identified the top three features were the "iron rim" sign of lesions, DD, and the T1/FLAIR ratios of the lesions.
conclusionThis study developed a machine learning model to predict the volume outcome of MS lesions. Feature importance analysis identified chronic inflammation around the lesion, DD, and the microstructural damage as key factors influencing volume change in chronic MS lesions. KEY POINTS: Question The evolution of different chronic lesions in MS exhibits variability, and the driving factors influencing these outcomes remain to be further investigated. Findings A SVM learning model was developed to predict chronic MS lesion volume changes, integrating lesion characteristics, lesion burden, and clinical data. Clinical relevance Chronic inflammation surrounding lesions, DD, and microstructural damage are key factors influencing the evolution of chronic MS lesions.
Indexed as
Identifiers
40528098What 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.