Hepatology research : the official journal of the Japan Society of Hepatology · 2026
Histological AI-Assisted Analysis Outperforms Pathologist Assessment in Predicting Disease Outcomes in PSC Patients.
СтатьяНазвание и аннотация: Перевод в очереди
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Salonen Anna M, Nyholm Iiris, Tähti Linn, Sinkko Matilda, Kumar Darshan, Färkkilä Martti, Arola Johanna, Pakarinen Mikko P, Boyd Sonja, Sjöblom Nelli
Аннотация
AIMS: Liver biopsy has shown prognostic significance in primary sclerosing cholangitis (PSC). However, histology is typically assessed manually based on coarse grading scales, making precise quantification difficult. We developed a neural network model to address the key histological features of liver tissue, including portal tracts, fibrosis, biliary epithelium, portal inflammation, and vasculature.
METHODS AND RESULTS: Our training data included 603 liver sections from patients with PSC (n = 478), other hepatobiliary diseases (n = 119), and normal controls (n = 6), ensuring heterogeneity of the sample material. The model was validated and evaluated for its ability to predict key clinical endpoints in PSC: liver transplantation (LT) due to end-stage liver disease (ESLD), and a composite endpoint comprising LT, cholangiocarcinoma (CCA), and liver-related death, allowing evaluation with all relevant clinical outcomes. The model reliably identified the targeted histological features. AI-derived portal inflammation and fibrosis demonstrated superior discriminatory performance compared with manual scoring, particularly for prediction of LT due to ESLD (AUC 0.81-0.82 vs. 0.86-0.87). AI-derived blood vessels and biliary epithelium were also prognostic for LT due to ESLD (AUC 0.80, 0.83). In multivariable analysis adjusted for age and sex, AI-derived portal inflammation remained independently associated with the composite endpoint (HR 2.51, 95% CI 1.04-6.05).
CONCLUSIONS: AI-measured portal inflammation is a robust predictor of clinical endpoints. Additionally, vascular area, fibrosis, and biliary epithelium showed prognostic value. AI-based measures outperformed manual scores for several parameters. Overall, multiple AI-derived parameters predicted LT due to ESLD, and comparable trends were observed for the composite endpoint. These findings highlight the importance of liver histology as a surrogate endpoint in patients with PSC.
METHODS AND RESULTS: Our training data included 603 liver sections from patients with PSC (n = 478), other hepatobiliary diseases (n = 119), and normal controls (n = 6), ensuring heterogeneity of the sample material. The model was validated and evaluated for its ability to predict key clinical endpoints in PSC: liver transplantation (LT) due to end-stage liver disease (ESLD), and a composite endpoint comprising LT, cholangiocarcinoma (CCA), and liver-related death, allowing evaluation with all relevant clinical outcomes. The model reliably identified the targeted histological features. AI-derived portal inflammation and fibrosis demonstrated superior discriminatory performance compared with manual scoring, particularly for prediction of LT due to ESLD (AUC 0.81-0.82 vs. 0.86-0.87). AI-derived blood vessels and biliary epithelium were also prognostic for LT due to ESLD (AUC 0.80, 0.83). In multivariable analysis adjusted for age and sex, AI-derived portal inflammation remained independently associated with the composite endpoint (HR 2.51, 95% CI 1.04-6.05).
CONCLUSIONS: AI-measured portal inflammation is a robust predictor of clinical endpoints. Additionally, vascular area, fibrosis, and biliary epithelium showed prognostic value. AI-based measures outperformed manual scores for several parameters. Overall, multiple AI-derived parameters predicted LT due to ESLD, and comparable trends were observed for the composite endpoint. These findings highlight the importance of liver histology as a surrogate endpoint in patients with PSC.