Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation

doi:10.3748/wjg.120562

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World Journal of Gastroenterology

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1007-9327

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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 Gastroenterol. Nov 21, 2026; 32(43): 120562
Published online Nov 21, 2026. doi: 10.3748/wjg.120562

Table 3 Comparative performance of the optimal preoperative clinical-radiomic model vs optimal models incorporating peri-ablation zones features, area under the curve (95% confidence interval)

		AUC	Sensitivity	Specificity	PPV	NPV	Accuracy	F1 score
Clinical + rad-preop	Training set	0.928 (0.893-0.964)	0.934 (0.882-0.964)	0.809 (0.715-0.877)	0.764 (0.713-0.832)	0.949 (0.803-0.968)	0.859	0.841
	Internal validation set	0.848 (0.742-0.955)	0.842 (0.696-0.926)	0.739 (0.535-0.875)	0.554 (0.503-0.773)	0.924 (0.835-0.975)	0.768	0.668
	External validation set	0.849 (0.747-0.951)	0.763 (0.608-0.87)	0.826 (0.629-0.93)	0.707 (0.698-0.847)	0.864 (0.747-0.897)	0.804	0.734
Clinical + rad-preop + peri-necrotic (5 mm)	Training set	0.957 (0.933-0.982)	0.921 (0.866-0.954)	0.888 (0.805-0.938)	0.845 (0.791-0.903)	0.944 (0.803-0.965)	0.901	0.881
	Internal validation set	0.894 (0.852-0.9935)	0.821 (0.752-0.874)	0.831 (0.74-0.895)	0.651 (0.613-0.734)	0.924 (0.839-0.979)	0.828	0.726
	External validation set	0.863 (0.761-0.965)	0.816 (0.666-0.908)	0.783 (0.581-0.903)	0.674 (0.615-0.839)	0.886 (0.724-0.927)	0.803	0.795
Clinical + rad-preop + peri-necrotic (10 mm)	Training set	0.942 (0.912-0.972)	0.894 (0.835-0.934)	0.854 (0.766-0.913)	0.803 (0.782-0.901)	0.924 (0.836-0.974)	0.87	0.846
	Internal validation set	0.898 (0.859-0.936)	0.815 (0.745-0.868)	0.843 (0.753-0.904)	0.666 (0.612-0.788)	0.922 (0.874-0.965)	0.835	0.733
	External validation set	0.876 (0.784-0.969)	0.947 (0.827-0.985)	0.652 (0.449-0.812)	0.59 (0.543-0.705)	0.957 (0.857-0.997)	0.757	0.734
Clinical + rad-preop + peri-necrotic (15 mm)	Training set	0.941 (0.908-0.974)	0.907 (0.85-0.944)	0.854 (0.766-0.913)	0.805 (0.743-0.849)	0.933 (0.856-0.969)	0.875	0.853
	Internal validation set	0.875 (0.784-0.967)	0.789 (0.637-0.889)	0.826 (0.629-0.93)	0.636 (0.612-0.782)	0.911 (0.815-0.941)	0.816	0.704
	External validation set	0.863 (0.763-0.962)	0.868 (0.727-0.942)	0.783 (0.581-0.903)	0.688 (0.627-0.742)	0.915 (0.831-0.958)	0.813	0.768
Clinical + rad-preop + peri-necrotic (20 mm)	Training set	0.986 (0.974-0.997)	0.94 (0.891-0.968)	0.955 (0.889-0.982)	0.933 (0.867-0.989)	0.960 (0.826-0.987)	0.949	0.937
	Internal validation set	0.792 (0.67-0.913)	0.868 (0.727-0.942)	0.696 (0.491-0.844)	0.523 (0.501-0.713)	0.932 (0.849-0.987)	0.744	0.653
	External validation set	0.795 (0.676-0.915)	0.658 (0.499-0.788)	0.864 (0.667-0.953)	0.727 (0.701-0.863)	0.821 (0.723-0.895)	0.791	0.691

Data are 95% confidence intervals in parentheses.

AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Full Size Table

Citation: Liu T, Wu C, Dong TT, Jia YY, Zhu YY, Wei CM, Duan Y, Li YX, Nie F. Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation. World J Gastroenterol 2026; 32(43): 120562
URL: https://www.wjgnet.com/1007-9327/full/v32/i43/120562.htm
DOI: https://dx.doi.org/10.3748/wjg.120562

Liu T, Wu C, Dong TT, Jia YY, Zhu YY, Wei CM, Duan Y, Li YX, Nie F. Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation. World J Gastroenterol 2026; 32(43): 120562 [DOI: 10.3748/wjg.120562]

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