1Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
2Department of Gastroenterology and Hepatology, Saiseikai Suita Hospital, 1-2 Kawazono-cho Suita, Osaka, 564-0013, Japan.
3Department of Pharmacology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
4Department of Gastroenterology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
5Department of Gastroenterology, Graduate School of Medicine, Kanazawa University, Kanazawa, Japan.
6Department of Human Pathology, Graduate School of Medicine, Kanazawa University, Kanazawa, Japan.
7Clinical Development, Gilead Sciences, Inc., Foster City, USA.
8Department of Gastroenterology and Hepatology, Saiseikai Suita Hospital, 1-2 Kawazono-cho Suita, Osaka, 564-0013, Japan. email@example.com.
Background: Fibro-Scope is an artificial intelligence/neural network system to determine the fibrosis stage in nonalcoholic steatohepatitis (NASH) using 12 parameters of the patient: age, sex, height, weight, waist circumference (WC), platelet count, and the levels of aspartate and alanine aminotransferase, gamma-glutamyltransferase, cholesterol, triglycerides, and type IV collagen 7S. However, measurement of WC is unstable and often missing from patient databases. Herein, we created Fibro-Scope V1.0.1 that has the same detection power as its predecessor, without the need to consider WC.
Methods: To build a new AI diagnostic system available for the global needs, data from 764 patients with NASH and bridging fibrosis (STELLAR-3) or compensated cirrhosis (STELLAR-4) that participated in two phase III trials were added to the Japanese data. Finally, the data of a total of 898 patients in the training and of 300 patients in the validation studies were analyzed, respectively.
Results: The discrimination of F0-2 from F3,4 through Fibro-Scope V1.0.1 was characterized by a 99.8% sensitivity, a 99.6% specificity, a 99.8% positive predictive value, and a 99.6% negative predictive value in a training study with gray zone analysis; similar effectiveness was also revealed in the analysis without a gray zone. In the validation studies with and without gray zone analysis, high sensitivity and specificity were also identified. Fibro-Scope V1.0.1 exerted a diagnostic accuracy for F3,4 advanced fibrosis that was comparable to that of the original Fibro-Scope and delivered high (> 92%) sensitivity and specificity.
Conclusion: Fibro-Scope V1.0.1 can accurately diagnose F3,4 fibrosis without the need of WC.