Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma

doi:10.35712/aig.120311

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Artificial Intelligence in Gastroenterology

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2644-3236

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

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 120311
Published online Aug 8, 2026. doi: 10.35712/aig.120311

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Figure 2 The framework illustrates the integration of radiomic, pathomic, and clinical data, along with methodological considerations, including validation, calibration, and causal inference, for clinical translation.

Citation: Meena BL, Behera B, Rudra OS, Sharma D. Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma. Artif Intell Gastroenterol 2026; 7(2): 120311
URL: https://www.wjgnet.com/2644-3236/full/v7/i2/120311.htm
DOI: https://dx.doi.org/10.35712/aig.120311

Meena BL, Behera B, Rudra OS, Sharma D. Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma. Artif Intell Gastroenterol 2026; 7(2): 120311 [DOI: 10.35712/aig.120311]

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