Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis. Issue 4 (27th December 2022)

• Record Type: Journal Article
• Authors: Sjöblom, Nelli; Boyd, Sonja; Manninen, Anniina; Blom, Sami; Knuuttila, Anna; Färkkilä, Martti; Arola, Johanna
• Is Part Of: Hepatology research. Volume 53:Issue 4(2023)
• Page Start: 322
• Page End: 333
• Publication Date: 2022-12-27
• Subjects: artificial intelligence -- ductular reaction -- liver histology -- primary sclerosing cholangitis -- surrogate marker; Liver -- Diseases -- Periodicals; Liver Diseases -- Periodicals; Foie -- Maladies -- Périodiques; 616.362
• DOI: 10.1111/hepr.13867
• Languages: English
• ISSNs: 1386-6346

Abstract

Abstract: Background and Aims: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)‐based models. Methods: A K7‐AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7‐stained PSC liver biopsy specimens ( n = 295) were analyzed. A compound endpoint (liver transplantation, liver‐related death, and cholangiocarcinoma) was applied in Kaplan–Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. Results: The K7‐AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7‐positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. Conclusions: The K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7‐AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials. Abstract : Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to liver cirrhosis or cholangiocarcinoma. Here, we developed a K7‐AI model 2.0 to analyze K7‐stained liver specimens of patients with PSC. We found that the K7‐AI model 2.0 can serve as a prognostic tool for clinical endpoints, since it was able to provide significant information on disease outcomes based on different histological features.