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Dig Liver Dis ; 55(3): 373-380, 2023 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-36357293

RESUMEN

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.


Asunto(s)
Pancreatocolangiografía por Resonancia Magnética , Colangitis Esclerosante , Humanos , Pancreatocolangiografía por Resonancia Magnética/métodos , Colangitis Esclerosante/complicaciones , Estudios Retrospectivos , Inteligencia Artificial , Pronóstico
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