ArticleLiver international : official journal of the International Association for the Study of the Liver2025
Radiomics-Based Prognostication in Primary Sclerosing Cholangitis: A Proof-of-Concept Study.
Article in Liver international : official journal of the International Association for the Study of the Liver, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Pharmacokinetic modelling of MRI-based liver function for risk assessment in primary sclerosing cholangitis: a prospective pilot study.European radiology experimental · 2026Article
- Primary Sclerosing Cholangitis: Diagnosis, Management, and Clinical Challenges.Journal of clinical medicine · 2026Review
- Radiomics-Based Prognostication in Primary Sclerosing Cholangitis: A Proof-of-Concept Study.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
BACKGROUND AND
aimRisk assessment in primary sclerosing cholangitis (PSC) by magnetic resonance imaging (MRI) relies on semi-quantitative analysis, which can result in interpretation variability. Radiomics may offer a quantitative approach for risk stratification. This study aims to explore and validate MRI-derived radiomic features to identify high-risk PSC patients.
methodsIn this prospective study (January 2019-December 2022), consecutive PSC patients undergoing routine gadoxetate disodium-enhanced MRI were recruited. Using PyRadiomics, whole liver parenchyma features were extracted from five MRI sequences according to the Image Biomarker Standardisation Initiative (IBSI). Patients were categorised into risk groups based on the Mayo risk score (MRS) and liver stiffness measurement (LSM). Features associated with high-risk patients were selected and validated in an independent cohort. A survival analysis was conducted in the combined cohort to assess the prognostic value of the radiomic features for clinical events.
resultsOne hundred and two PSC patients were enrolled in this study. Five radiomics features were associated with high risk in the training cohort. In the validation setting, GLRLM-Run Entropy in the fat-saturation T2 weighted imaging (FS-T2W) sequence was the only significant feature, with an odds ratio of 3.90 (CI 1.46-10.42, p = 0.007) for MRS and 2.97 (CI 1.33-6.66, p = 0.008) for LSM. Its prognostic potential on clinical outcome was confirmed by Cox regression analysis in the combined cohort (hazard ratio per 0.1 increase = 1.480, CI 1.226-1.786), showing excellent predictive performance (C-index = 0.857).
conclusionsGLRLM-Run Entropy in FS-T2W is a novel radiomics-based biomarker for risk stratification in PSC patients. It is quantitative, standardised, easy to compute and cost-free, positioning it as a potential key innovation in PSC radiology-based biomarkers.
trial registrationClinicaltrial.gov ID: NC705618145.
Indexed as
Identifiers
What 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.