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Background: The GALAD score is a serum biomarkerbased model that predicts the probability of having hepatocellular carcinoma (HCC) in patients with chronic liver disease. We aimed to assess the performance of the GALAD score in comparison with liver ultrasound for detection of HCC.Methods: A single-center cohort of 111 HCC patients and 180 controls with cirrhosis or chronic hepatitis B and a multicenter cohort of 233 early HCC and 412 cirrhosis patients from the Early Detection Research Network (EDRN) phase II HCC Study were analyzed.Results: The area under the ROC curve (AUC) of the GALAD score for HCC detection was 0.95 [95% confidence interval (CI), 0.93-97], which was higher than the AUC of ultrasound (0.82, P <0.01). At a cutoff of À0.76, the GALAD score had a sensitivity of 91% and a specificity of 85% for HCC detection. The AUC of the GALAD score for early-stage HCC detection remained high at 0.92 (95% CI, 0.88-0.96; cutoff À1.18, sensitivity 92%, specificity 79%). The AUC of the GALAD score for HCC detection was 0.88 (95% CI, 0.85-0.91) in the EDRN cohort. The combination of GALAD and ultrasound (GALADUS score) further improved the performance of the GALAD score in the single-center cohort, achieving an AUC of 0.98 (95% CI, 0.96-0.99; cutoff À0.18, sensitivity 95%, specificity 91%). a For the calculation of AUC, the continuous GALAD score is used, whereas for sensitivity and specificity, we used the GALAD cutoff. b P value looking at difference in AUC between GALAD and ultrasound.Yang et al.

Background: Primary sclerosing cholangitis (PSC) is a major risk factor for cholangiocarcinoma (CCA). We investigated biliary and fecal microbiota to determine whether specific microbes in the bile or stool are associated with PSC or CCA. Methods: Bile was obtained from 32 patients with PSC, 23 with CCA with PSC, 26 with CCA without PSC, and 17 controls. Over 90% of bile samples were from patients with perihilar CCA. Stool was obtained from 31 patients with PSC (11 were matched to bile), 16 with CCA with PSC (10 matched to bile), and 11 with CCA without PSC (6 matched to bile). Microbiota composition was assessed using 16SrRNA-marker-based sequencing and was compared between groups. Results: Bile has a unique microbiota distinguished from negative DNA controls and stool. Increased species richness and abundance of Fusobacteria correlated with duration of PSC and characterized the biliary microbiota in CCA. Stool microbiota composition showed no significant differences between groups. Conclusions: We identified a unique microbial signature in the bile of patients with increased duration of PSC or with CCA, suggesting a role for microbiota-driven inflammation in the pathogenesis and or progression to perihilar CCA. Further studies are needed to test this hypothesis.

Objective: Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related mortality worldwide, and a rising cause of cancer mortality in the U.S. Liver cirrhosis is the major risk factor for HCC. Surveillance of persons with cirrhosis facilitates early detection and improves outcomes. We assessed the surveillance rate for HCC within a major academic health system and identified factors influencing surveillance. Patients and Methods: We examined the surveillance rate for HCC using liver ultrasound, CT, or MRI, and factors influencing surveillance in a cohort of 369 Minnesota residents with cirrhosis seen at the Mayo Clinic between 2007 and 2009. Results: Ninety-three percent of cirrhosis patients received at least one surveillance study, but only 14% received the recommended uninterrupted semiannual surveillance. Thirty percent received ≥75% of recommended surveillance, and 59% received ≥50% of recommended surveillance. Factors increasing surveillance included gastroenterology or hepatology specialist visits (p < 0.0001), advanced liver disease as assessed by hepatic encephalopathy (p = 0.0008), and comorbid illness as assessed by diabetes mellitus (p = 0.02). Age, sex, race, residence, cirrhosis etiology, or number of primary care visits did not significantly affect the rate of surveillance. Conclusions: While the rate of surveillance in a major academic health system was higher than reported in other studies, surveillance was heavily dependent on visits to a subspecialist. This suggests a major and urgent national need to improve identification of individuals at risk for HCC in the primary care setting and the initiation and maintenance of surveillance by primary care practitioners.

Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).