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

Retrospective Cohort Study

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

Table 3 The DeLong test comparisons among different models in the training, internal validation, and external validation cohorts

Model		Training		Internal validation		External validation
Model		Z value	P value	Z value	P value	Z value	P value
Cli	10 mm	-3.916	< 0.0001	-2.310	0.0208	-2.212	0.0230
Cli	Combined	-4.948	< 0.0001	-3.498	0.0005	-3.035	0.0024
10 mm	Combined	-1.086	0.2773	-1.063	0.2878	-0.798	0.4249

Cli: Clinical imaging model; 10 mm: 10 mm peritumoral model; Combined: Model integrating clinical imaging features and 10 mm per itumoral features.

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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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