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

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Figure 2 Flowchart of the process from region of interest delineation on original ultrasound images to final prediction model construction. ROI: Region of interest; US: Ultrasound; CEUS: Contrast-enhanced ultrasound; SVM: Support vector machine; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting; DT: Decision tree; RF: Random forest; LR: Logistic regression; KNN: K-nearest neighbors.

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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