Deep learning-enhanced peritumoral radiomics predicts early recurrence after hepatocellular carcinoma ablation: A two-center study

doi:10.4251/wjgo.122464

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World Journal of Gastrointestinal Oncology

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1948-5204

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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.

World J Gastrointest Oncol. Oct 15, 2026; 18(10): 122464
Published online Oct 15, 2026. doi: 10.4251/wjgo.122464

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Figure 1 Research flowchart. A: Volume of interest segematation; B: Features extraction; C: Features selection; D: Model construction. LASSO: Least absolute shrinkage and selection operator; AUC: Area under the curve; VOI: Volume of interest.

Citation: Li YH, Yao L, Qian GX, Lei XD, Tang ZQ, Guo BY, Du R, Zhu Y, Jia WD. Deep learning-enhanced peritumoral radiomics predicts early recurrence after hepatocellular carcinoma ablation: A two-center study. World J Gastrointest Oncol 2026; 18(10): 122464
URL: https://www.wjgnet.com/1948-5204/full/v18/i10/122464.htm
DOI: https://dx.doi.org/10.4251/wjgo.122464

Li YH, Yao L, Qian GX, Lei XD, Tang ZQ, Guo BY, Du R, Zhu Y, Jia WD. Deep learning-enhanced peritumoral radiomics predicts early recurrence after hepatocellular carcinoma ablation: A two-center study. World J Gastrointest Oncol 2026; 18(10): 122464 [DOI: 10.4251/wjgo.122464]

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