An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials

Abstract

Background: Many clinical trials with potential drug treatment options for non-alcoholic fatty liver disease (NAFLD) are focused on patients with non-alcoholic steatohepatitis (NASH) stages 2 and 3 fibrosis. As the histological features differentiating stage 1 (F1) from stage 2 (F2) NASH fibrosis are subtle, some patients may be wrongly staged by the in-house pathologist and miss the opportunity for enrollment into clinical trials. We hypothesized that our refined artificial intelligence (AI)-based algorithm (qFibrosis) can identify these subtle differences and serve as an assistive tool for in-house pathologists. Methods: Liver tissue from 160 adult patients with biopsy-proven NASH from Singapore General Hospital (SGH) and Peking University People’s Hospital (PKUH) were used. A consensus read by two expert hepatopathologists was organized. The refined qFibrosis algorithm incorporated the creation of a periportal region that allowed for the increased detection of periportal fibrosis. Consequently, an additional 28 periportal parameters were added, and 28 pre-existing perisinusoidal parameters had altered definitions. Results: Twenty-eight parameters (20 periportal and 8 perisinusoidal) were significantly different between the F1 and F2 cases that prompted a change of stage after a careful consensus read. The discriminatory ability of these parameters was further demonstrated in a comparison between the true F1 and true F2 cases as 26 out of the 28 parameters showed significant differences. These 26 parameters constitute a novel sub-algorithm that could accurately stratify F1 and F2 cases. Conclusion: The refined qFibrosis algorithm incorporated 26 novel parameters that showed a good discriminatory ability for NASH fibrosis stage 1 and 2 cases, representing an invaluable assistive tool for in-house pathologists when screening patients for NASH clinical trials.

Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

“…qFibrosis has shown its ability to perform accurate fibrotic scoring of hepatic tissue in animal models and chronic hepatitis B patients. Besides, it had been established as a better way for screening and enrollment of NASH patients in clinical trials [ 21 , 22 ]. Our results using the recurrence prediction model in 64 HCC patients after hepatectomy indicated that early recurrence can be predicted when the combined index is more than 0.501.…”

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

“…An integrated AI‐based automated tool is able to detect and quantify liver fibrosis through collagen proportionate area and assess its architectural pattern in liver biopsy with accuracy above 90% for bridging fibrosis and histological cirrhosis 29 . A refined AI‐based algorithm (qFibrosis) incorporated 26 novel histological periportal parameters that showed a good discriminatory ability for earlier stages of liver fibrosis in NAFLD (F1 vs F2) 30 . This would be particularly useful for screening patients for NAFLD clinical trials, which specifically target patients with F2 fibrosis.…”

“…Other investigators and companies have relied instead on digitized images of stained biopsy slides 20,29,30,33 . While most systems provide a consensus score based on the most likely fibrosis stage, the PathAI system describes the proportion of areas with features most resembling different particular fibrosis stages.…”

Management of NAFLD patients with advanced fibrosis

“…12 Q-fibrosis, a technique that has been shown to improve the underestimation of staging in suboptimal biopsies (<15 mm) and under-and over-scoring by different pathologists (P < .001), has recently been modified and applied to NAFLD to improve the discrimination between F1 and F2 patients. 13 Liver biopsy is still the best method to evaluate the progression and regression of fibrosis but it is limited by cost, accuracy, a risk of adverse events and invasiveness so that it is unsuitable for large-scale screening. which use mechanical drivers to generate shear wave and measure its velocity using sonographic Doppler or MR techniques, and shear wave elastography (pSWE 2D-SWE, 3D-SWE), which uses high frequency ultrasound impulses for shear wave generation from one or multiple frequencies in real-time using ultrasound.…”

“…Morphometry provides a finite‐quantitative scale of the amounts of collagen, the Collagen Proportionate Area (CPA), which has already been used in certain clinical trials for Hepatitis C but it is time consuming and has a non‐linear relationship with the stage of fibrosis 12 . Q‐fibrosis, a technique that has been shown to improve the underestimation of staging in suboptimal biopsies (<15 mm) and under‐ and over‐scoring by different pathologists ( P < .001), has recently been modified and applied to NAFLD to improve the discrimination between F1 and F2 patients 13 . Liver biopsy is still the best method to evaluate the progression and regression of fibrosis but it is limited by cost, accuracy, a risk of adverse events and invasiveness so that it is unsuitable for large‐scale screening.…”