A quantitative MRCP-derived score for medium-term outcome prediction in primary sclerosing cholangitis

Abstract

Background: Magnetic resonance cholangiopancreatography (MRCP) is the gold standard for diagnosis of patients with primary sclerosing cholangitis (PSC). The semi-quantitative MRCP-derived Anali scores proposed for risk stratification, have poor-to-moderate inter-reader agreement.

Aims: To evaluate the prognostic performance of quantitative MRCP metrics in PSC.

Methods: This is a retrospective study of PSC patients undergoing MRCP. Images were processed using MRCP+ software (Perspectum Ltd, Oxford) that provides quantitative biliary features, semi-automatically extracted by artificial intelligence-driven analysis of MRCP-3D images. The prognostic value of biliary features has been assessed for all hepato-biliary complications.

Results: 87 PSC patients have been included in the analysis. Median follow-up from MRCP to event/censoring of 30.9 months (Q1-Q3=13.6-46.6). An adverse outcome occurred in 27 (31.0%) patients. The number of biliary strictures (HR=1.05 per unit, 95%CI 1.02-1.08, p < 0.0001), spleen length (HR=1.16 per cm, 95%CI 1.01-1.34, p = 0.039), adjusted for height, age at MRCP, and time from diagnosis to MRCP predicted higher risk of hepatobiliary complications. These were incorporated into a the quantitative MRCP-derived PSC (qMRCP-PSC) score (C-statistic=0.80). After 3-fold cross-validation, qMRCP-PSC outperformed the Anali score in our cohort (C-statistic of 0.78 vs 0.64) and enabled the discrimination of survival of PSC patients (log-rank p < 0.0001).

Conclusions: The qMRCP-PSC score identified patients at higher risk of hepatobiliary complications and outperformed the available radiological scores. It represents a novel quantitative biomarker for disease monitoring and a potential surrogate endpoint for clinical trials.

Keywords: Artificial intelligence; MRCP; Primary sclerosing cholangitis; Prognostic score.