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]
Corresponding Author of This Article
Fang Nie, PhD, Professor, Ultrasound Medicine Center, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou 730000, Gansu Province, China. ery_nief@lzu.edu.cn
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Oncology
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research-article
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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]
World J Gastroenterol. Nov 21, 2026; 32(43): 120562 Published online Nov 21, 2026. doi: 10.3748/wjg.120562
Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation
Ting Liu, Tian-Tian Dong, Ying-Ying Jia, Yang-Yang Zhu, Chuan-Min Wei, Fang Nie, Ultrasound Medicine Center, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
Chuang Wu, Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
Ying Duan, Ultrasound Medicine Center, Gansu Provincial Cancer Hospital, Lanzhou 730000, Gansu Province, China
Yong-Xin Li, School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Co-first authors: Ting Liu and Chuang Wu.
Author contributions: Liu T and Wu C conceived and designed the study as co-first authors; Nie F provided administrative support; Dong TT provided study materials; Zhu YY, Jia YY, Wei CM, and Duan Y collected and assembled data; Wu C and Li YX analyzed and interpreted data. All authors have read and approve the final manuscript.
Supported by Key Research and Development Program of Gansu Province, China, No. 21YF5FA122.
Institutional review board statement: This study protocol was approved by the Institutional Ethics Committee of the Lanzhou University Second Hospital, No. 2026A-219.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data are not publicly available due to ongoing research.
Corresponding author: Fang Nie, PhD, Professor, Ultrasound Medicine Center, Lanzhou University Second Hospital, No. 82 Cuiyingmen, Chengguan District, Lanzhou 730000, Gansu Province, China. ery_nief@lzu.edu.cn
Received: March 4, 2026 Revised: April 15, 2026 Accepted: June 15, 2026 Published online: November 21, 2026 Processing time: 207 Days and 9.3 Hours
Abstract
BACKGROUND
Hepatocellular carcinoma (HCC) remains a global health challenge. Thermal ablation (TA) is a guideline-recommended curative treatment for early-stage HCC. Early recurrence after TA, often arising from occult metastases missed during initial staging, is associated with a poorer prognosis. In this context, noninvasive imaging plays a critical role in assessing treatment response and stratifying recurrence risk in HCC.
AIM
To develop and validate a radiomics model integrating pre- and post-ablation features for predicting early HCC recurrence after TA.
METHODS
This retrospective study enrolled 301 patients who underwent initial TA for HCC and divided them into training (n = 168), internal validation (n = 72), and external validation (n = 61) sets. Radiomic features were extracted from pre-operative ultrasound and contrast-enhanced ultrasound images of the tumor, as well as from postoperative contrast-enhanced ultrasound images of four peri-ablation margins (5-20 mm from the ablation zone). Seven classifiers integrating clinical, pre-operative radiomic, and margin features were developed. Model performance was assessed using area under the curve (AUC), calibration, and decision curve analysis, followed by SHapley Additive exPlanations analysis.
RESULTS
A total of 301 patients, including 109 with ER, were included. The baseline model, based solely on pre-operative clinical and radiomic features, yielded an AUC of 0.849. Integration of features from concentric peri-ablation zones showed that model improvement depended on the spatial extent of the analyzed region. The optimal 10-mm peri-ablation support vector machine (SVM) model achieved the highest test-set AUC of 0.876 (95% confidence interval: 0.784-0.969), significantly outperforming the preoperative-only baseline model (AUC = 0.849). Models incorporating the 5-mm (AUC = 0.863) and 15-mm (AUC = 0.863) margins showed similar, though slightly lower, performance, whereas extending the analysis to a 20-mm margin resulted in a marked decline in performance (AUC = 0.795). SHapley Additive exPlanations analysis identified five top predictive radiomic features derived from pre-operative multiphase and postoperative portal-phase images.
CONCLUSION
Integrating pre-operative ultrasound with 10-mm peri-ablation radiomics effectively predicts early HCC recurrence after TA and may serve as a potential biomarker for risk stratification.
Core Tip: Pre-operative ultrasound and contrast-enhanced ultrasound radiomic features can effectively predict early recurrence of hepatocellular carcinoma after thermal ablation. Imaging features of the 10-mm peri-ablation zone, when combined with pre-operative features, provide significant incremental predictive value and may help guide personalized adjuvant therapy and surveillance strategies.
