Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis

doi:10.3748/wjg.v32.i5.113592

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Feb 7, 2026; 32(5): 113592
Published online Feb 7, 2026. doi: 10.3748/wjg.v32.i5.113592

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Figure 1 Risk factors for hepatocellular carcinoma. AFC: Advanced fibrosis cirrhosis; GPD: Genetic predisposition; HBV: Hepatitis B virus; HCV: Hepatitis C virus; MASLD: Metabolic associated steatotic liver disease. The image materials are sourced from Biorender (https://biorender.com) and Figdraw (https://www.figdraw.com) (Supplementary material).

Citation: Chen Y, Zhang Q, Zhang MY. Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis. World J Gastroenterol 2026; 32(5): 113592
URL: https://www.wjgnet.com/1007-9327/full/v32/i5/113592.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i5.113592

Chen Y, Zhang Q, Zhang MY. Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis. World J Gastroenterol 2026; 32(5): 113592 [PMID: 41693978 DOI: 10.3748/wjg.v32.i5.113592]

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