Genome Wide Association Study Identifies Genetic Variants Associated With Early And Sustained Response To (Peg)Interferon In Chronic Hepatitis B Patients: The GIANT-B Study

(Peg)interferon ((Peg)IFN) therapy leads to response in a minority of chronic hepatitis B (CHB) patients. Host genetic determinants of response are therefore in demand

Quantitative assessment of liver fibrosis: invasive or non-invasive?

Precise and quantitative assessment of the severity of liver fibrosis is of great value for confirming diagnosis, making treatment decision, monitoring treatment outcome, and determining prognosis. This paper analyzes the development and application of invasive and non-invasive quantitative assessment of histological liver fibrosis. From the first descriptive histological evaluation, the assessment of liver fibrosis based on invasive liver biopsy has been developed into semi-quantitative approaches. The fully-quantitative and objective assessment of histological liver fibrosis can overcome drawbacks of traditional semi-quantitative approaches including observer bias and sampling error. The non-invasive assessment of liver fibrosis not only overcomes drawbacks of liver biopsy including invasion and sampling error, but also holds promise for predicting the long-term outcome of disease, though its application in monitoring dynamic changes in liver fibrosis still needs to be confirmed by liver histological evaluation. Therefore, the non-invasive assessment of liver fibrosis cannot yet completely replace liver histological evaluation, but it can reduce the number of liver biopsies in patients with definitive diagnosis. The application of the non-invasive diagnostic method in monitoring dynamic changes in liver fibrosis needs to be validated at more clinical settings, and for the quantitative assessment of histological liver fibrosis, the correlation of clinical relevance between the results and the long-term outcomes needs to be systematically identified

qFibrosis: A fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients

Background & Aims: There is increasing need for accurate assessment of liver fibrosis/cirrhosis. We aimed to develop qFibrosis, a fully-automated assessment method combining quantification of histopathological architectural features, to address unmet needs in core biopsy evaluation of fibrosis in chronic hepatitis B (CHB) patients. Methods: qFibrosis was established as a combined index based on 87 parameters of architectural features. Images acquired from 25 Thioacetamide-treated rat samples and 162 CHB core biopsies were used to train and test qFibrosis and to demonstrate its reproducibility. qFibrosis scoring was analyzed employing Metavir and Ishak fibrosis staging as standard references, and collagen proportionate area (CPA) measurement for comparison. Results: qFibrosis faithfully and reliably recapitulates Metavir fibrosis scores, as it can identify differences between all stages in both animal samples (p <0.001) and human biopsies (p <0.05). It is robust to sampling size, allowing for discrimination of different stages in samples of different sizes (area under the curve (AUC): 0.93–0.99 for animal samples: 1–16 mm[superscript 2]; AUC: 0.84–0.97 for biopsies: 10–44 mm in length). qFibrosis can significantly predict staging underestimation in suboptimal biopsies (<15 mm) and under- and over-scoring by different pathologists (p <0.001). qFibrosis can also differentiate between Ishak stages 5 and 6 (AUC: 0.73, p = 0.008), suggesting the possibility of monitoring intra-stage cirrhosis changes. Best of all, qFibrosis demonstrates superior performance to CPA on all counts. Conclusions: qFibrosis can improve fibrosis scoring accuracy and throughput, thus allowing for reproducible and reliable analysis of efficacies of anti-fibrotic therapies in clinical research and practice.Janssen Pharmaceutical Ltd.Singapore-MIT Alliance for Research and Technolog

Early warning of hepatocellular carcinoma in cirrhotic patients by three-phase CT-based deep learning radiomics model: a retrospective, multicentre, cohort studyResearch in context

Summary: Background: The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis. Methods: We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3–12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703). Findings: We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918–0.941) in the discovery cohort, 0.902 (0.818–0.987) in the internal validation cohort, and 0.918 (0.898–0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P < 0.001). Furthermore, ALARM also demonstrated consistent performance in subgroup analysis. Interpretation: The novel ALARM model, based on deep learning radiomics with clinical variables, provides reliable estimates of short-term HCC development for cirrhotic patients, and may have the potential to improve the precision in clinical decision-making and early initiation of HCC treatments. Funding: This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), and the National Natural Science Foundation of China (82170610), Guangdong Basic and Applied Basic Research Foundation (2023A1515011211)

Genetic Variation rs16937012 near the NCOA2 Gene Predicts Sustained Response to Interferon in Chronic Hepatitis B patients: the GIANT-B study

WOS: 00036701320000

Genome-wide Association Study Identifies Genetic Variants Associated With Early and Sustained Response to (Pegylated) Interferon in Chronic Hepatitis B Patients: The GIANT-B Study

Background. (Pegylated) Interferon ([Peg]IFN) therapy leads to response in a minority of chronic hepatitis B (CHB) patients. Host genetic determinants of response are therefore in demand