BACKGROUND & AIMS: In some individuals with undetectable serum levels of hepatitis B surface antigen (HBsAg), hepatitis B virus (HBV) DNA can still be detected in serum or hepatocytes and HBV replicates at low levels-this is called occult HBV infection (OBI). OBI has been associated with increased risk of hepatocellular carcinoma (HCC). We investigated the incidence of OBI in patients with HCC and other liver diseases. We also investigated whether, in patients with OBI and HCC, HBV DNA has integrated into the DNA of hepatocytes. METHODS: We collected clinical information and liver tissues from 110 HBsAg-negative patients (90 with HCC and 20 without HCC; median ages at surgical resection and biopsy collection, 64.1 and 48.6 years, respectively) who underwent liver resection or liver biopsy from November 2002 through July 2017 in Hong Kong. HBV DNA and covalently closed circular DNA (cccDNA) were analyzed and quantified by PCR in liver tissues. Integration of HBV DNA into the DNA of liver cells was detected by Alu-PCR. RESULTS: Of the 90 HBsAg-negative patients with HCC, 18 had alcoholic liver disease (20%), 14 had nonalcoholic fatty liver disease or steatohepatitis (16%), 2 had primary biliary cholangitis, 2 had recurrent pyogenic cholangitis, 1 had autoimmune hepatitis, and 53 had none of these (59%). Among the 20 patients without HCC, 7 had non-alcoholic fatty liver disease or steatohepatitis, 7 had primary biliary cholangitis, and 6 had autoimmune hepatitis. OBI was detected in 62/90 patients with HCC (69%) and 3/20 patients without HCC (15%) (P < .0001). cccDNA was detectable in liver cells of 29 patients with HCC and OBI (47%) and HBV DNA had integrated into DNA of liver cells of 43 patients with HCC and OBI (69%); cccDNA and integrated HBV DNA were not detected in the 3 patients who had OBI without HCC. There were 29 patients with integration of HBV DNA among 33 patients with undetectable cccDNA in liver tissues (88%) and 14 patients with integration of HBV DNA among the 29 patients with cccDNA in liver tissues (48%) (P [ .001). HBV DNA was found to integrate near genes associated with hepatocarcinogenesis, such as those encoding telomerase reverse transcriptase, lysine methyltransferase 2B, and cyclin A2. Among the 43 patients with integration of HBV DNA, 39 (91%) did not have cirrhosis. CONCLUSIONS: In an analysis of clinical data and liver tissues from 90 HBsAg-negative patients with HCC, we found that almost 70% had OBI, of whom 70% had integration of HBV DNA into liver cell DNA; 90% of these patients did not have cirrhosis. HBV DNA integrated near hepatic oncogenes; these integrations might promote development of liver cancer.

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Background and study aims Artificial intelligence (AI)-assisted image classification has been shown to have high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of an AI-assisted image classifier on training of junior endoscopists for histological prediction of gastric lesions. Methods An AI image classifier was built on a convolutional neural network with five convolutional layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified endoscopic gastric images. The independent validation set consisted of another 1,000 endoscopic images from 100 gastric lesions. The first part of the validation set was reviewed by six junior endoscopists and the prediction of AI was then disclosed to three of them (Group A) while the remaining three (Group B) were not provided this information. All endoscopists reviewed the second part of the validation set independently. Results The overall accuracy of AI was 91.0 % (95 % CI: 89.2–92.7 %) with 97.1 % sensitivity (95 % CI: 95.6–98.7%), 85.9 % specificity (95 % CI: 83.0–88.4 %) and 0.91 area under the ROC (AUROC) (95 % CI: 0.89–0.93). AI was superior to all junior endoscopists in accuracy and AUROC in both validation sets. The performance of Group A endoscopists but not Group B endoscopists improved on the second validation set (accuracy 69.3 % to 74.7 %; P = 0.003). Conclusion The trained AI image classifier can accurately predict presence of neoplastic component of gastric lesions. Feedback from the AI image classifier can also hasten the learning curve of junior endoscopists in predicting histology of gastric lesions.