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"Sun Kyung Jeon"

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"Sun Kyung Jeon"

Original Articles
Non-contrast magnetic resonance imaging for detection of late recurrent hepatocellular carcinoma after curative treatment: a prospective multicenter comparison to contrast-enhanced computed tomography
Dong Wook Kim, Won Chang, So Yeon Kim, Young-Suk Lim, Jonggi Choi, Jungheum Cho, Jin-Wook Kim, Jai Young Cho, Sun Kyung Jeon, Yun Bin Lee, Eun Ju Cho, Su Jong Yu, Kyung-Suk Suh, Kwang-Woong Lee, Dong Ho Lee
Clin Mol Hepatol 2025;31(4):1285-1297.
Published online June 13, 2025
DOI: https://doi.org/10.3350/cmh.2025.0258
Background/Aims
Hepatocellular carcinoma (HCC) frequently recurs after curative treatment, posing challenges to long-term survival. Although contrast-enhanced multiphasic computed tomography (CECT) is commonly used for detecting recurrence, it is associated with risks such as radiation exposure and contrast agent reactions. This study aimed to compare the diagnostic performance of non-contrast magnetic resonance imaging (NC-MRI) with CECT for detecting recurrent HCC.
Methods
In this prospective multicenter intra-individual head-to-head comparison trial (study identifier: NCT05690451, KCT0006395), participants who had undergone curative treatment for HCC and remained recurrence-free for over two years were enrolled. Each participant underwent three follow-up imaging sessions at 2–6-month intervals using both CECT and NC-MRI. The primary outcome was the detection accuracy of each modality, analyzed using the generalized estimating equation analysis. Secondary outcomes included sensitivity and specificity.
Results
The study included 203 participants with a total of 528 paired imaging sessions, identifying recurrent HCC in 22 cases (10.8%). Among these, 21 cases involved intrahepatic recurrence with a median tumor size of 1.3 cm, and one case had aortocaval lymph node metastasis. NC-MRI achieved a detection accuracy of 96.6% (196/203), higher than CECT’s 91.6% (186/203) (P=0.006). NC-MRI also showed greater sensitivity (77.3% [17/22] vs. 36.4% [8/22]; P=0.012), while specificity was comparable between NC-MRI and CECT (98.9% [179/181] vs. 98.3% [178/181]; P=0.999).
Conclusions
NC-MRI demonstrated higher sensitivity and accuracy compared to CECT in detecting recurrent HCC in patients who had been disease-free for over two years following curative treatment, indicating its potential as a preferred imaging modality for this purpose.

Citations

Citations to this article as recorded by  Crossref logo
  • Non-contrast magnetic resonance imaging outperforms contrast-enhanced computed tomography in preoperative detection of hepatocellular carcinoma: A paired validation study
    Laizhu Zhang, Weiwei Zong, Jialin Gao, Huan Li, Leizhou Xia, Xiaoli Mai, Jun Chen, Binghua Li, Decai Yu
    Clinical and Molecular Hepatology.2026; 32(1): e34.     CrossRef
  • Updates in Abbreviated MRI‐Based HCC Surveillance
    Hyo Jung Park, So Yeon Kim, Young‐Suk Lim
    Journal of Gastroenterology and Hepatology.2026; 41(3): 914.     CrossRef
  • Moving beyond Ultrasound for Hepatocellular Carcinoma Surveillance in High-Risk Patients with Chronic Hepatitis B
    Hae Lim Lee
    Gut and Liver.2026; 20(2): 174.     CrossRef
  • Correspondence to editorial on “Non-contrast magnetic resonance imaging for detection of late recurrent hepatocellular carcinoma after curative treatment: a prospective multicenter comparison to contrast-enhanced computed tomography”
    Dong Ho Lee
    Clinical and Molecular Hepatology.2026; 32(2): e221.     CrossRef
  • Correspondence to letter to the editor on “Non-contrast magnetic resonance imaging for detection of late recurrent hepatocellular carcinoma after curative treatment: a prospective multicenter comparison to contrast-enhanced computed tomography”
    Dong Ho Lee
    Clinical and Molecular Hepatology.2026; 32(2): e251.     CrossRef
  • Beyond diagnostic accuracy: Economic and clinical considerations for NC-MRI in late HCC recurrence surveillance: Letter to the editor on “Non-contrast magnetic resonance imaging for detection of late recurrent hepatocellular carcinoma after curative treat
    Qi-Feng Chen, Sui-Xing Zhong, Ming Zhao
    Clinical and Molecular Hepatology.2026; 32(2): e175.     CrossRef
  • Interreader Agreement for Individual Ancillary Features in LI-RADS CT/MRI Version 2018: A Systematic Review and Meta-Analysis
    Yong Jun Jung, Sang Hyun Choi, Subin Heo, Dong Hwan Kim, Suk Kim, Seung Baek Hong
    American Journal of Roentgenology.2026;[Epub]     CrossRef
  • HCC surveillance after curative treatment: A new frontier for abbreviated MRI: Editorial on “Non-contrast magnetic resonance imaging for detection of late recurrent hepatocellular carcinoma after curative treatment: a prospective multicenter comparison to
    Hyungjin Rhee, Jin-Young Choi
    Clinical and Molecular Hepatology.2026; 32(2): 939.     CrossRef
  • Revisiting the role of non-contrast MRI in recurrent HCC: Bridging diagnostic performance and real-world applicability: Letter to the editor on “Non-contrast MRI for detection of late recurrent hepatocellular carcinoma after curative treatment: A prospect
    Lu Sun, Xin Zhang
    Clinical and Molecular Hepatology.2026; 32(2): e172.     CrossRef
  • Multicentre prospective trial of abbreviated MRI using gadoxetic acid versus CT for detection of late recurrent HCC (AMRICT): study protocol
    Hyo Jung Park, Dong Ho Lee, Won Chang, Hae Young Kim, Dong Hwan Kim, Won-Mook Choi, Sung Won Chung, Jonggi Choi, Danbi Lee, Ju Hyun Shim, Han Chu Lee, Young-Suk Lim, Seon-Ok Kim, Amit G Singal, So Yeon Kim
    BMJ Open.2026; 16(4): e116809.     CrossRef
  • The Use of Non‐Contrast Abbreviated MRI for the Detection of Recurrent Hepatocellular Carcinoma in Postoperative Surveillance
    Jianwei Chen, Yuemin Zhu, Dechun Zheng, Liting Wen, Juyi Wu, Qiuyuan Yue, Zhuting Fang
    Journal of Magnetic Resonance Imaging.2026;[Epub]     CrossRef
  • Performance of GAAD and GALAD Biomarker Panels for HCC Detection in Patients with MASLD or ALD Cirrhosis
    Mohammad Jarrah, Sneha Deodhar, Lisa Quirk, Mohammed Al-Hasan, Ashish Sharma, Guruveer Bhamra, Julia Terrell, Fasiha Kanwal, Yujin Hoshida, Nicole E. Rich, Purva Gopal, Amit G. Singal
    Cancers.2025; 17(23): 3835.     CrossRef
  • 9,836 View
  • 232 Download
  • 12 Web of Science
  • Crossref

Viral hepatitis

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection
Jeongin Yoo, Heejin Cho, Dong Ho Lee, Eun Ju Cho, Ijin Joo, Sun Kyung Jeon
Clin Mol Hepatol 2023;29(4):1029-1042.
Published online August 29, 2023
DOI: https://doi.org/10.3350/cmh.2023.0190
Background/Aims
The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB. Methods: This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses. Results: During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD. Conclusions: Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

Citations

Citations to this article as recorded by  Crossref logo
  • Artificial intelligence prognostication of liver disease using imaging
    Manil D Chouhan, Kate McLean, James A Thomas, Jason Dowling
    British Journal of Radiology.2026; 99(1182): 1036.     CrossRef
  • The atlas of abdominal organ remodeling in hepatocellular carcinoma patients: An artificial intelligence-based multicenter imaging study
    Yusheng Guo, Xiaona Fu, Tianxiang Li, Ning Wang, Shanshan Jiang, Shanmei Li, Xiaofang Guo, Rundong Wang, Shichao Long, Mengsi Li, Qiuping Liu, Kai Zhao, Yangyang Xie, Xuejun Chen, Lixia Wang, Chuansheng Zheng, Lian Yang
    Med.2026; 7(7): 101176.     CrossRef
  • Dynamic changes in visceral fat density and skeletal muscle index predict new-onset decompensation in chronic hepatitis B-related cirrhosis
    Xiaoyue Zhang, Zheyu Li, Cuifang He, Shangwen Hu, Yirui Hu, Huixia Zhang, Qianhui Gao, Wanchun Qiu, Wenqiang He, Zhenxia Huang, Meiling Zhang, Jiale Lu, Liting Zhang, Junfeng Li
    Hepatobiliary Communications.2026; 1(1): 100003.     CrossRef
  • Routine laboratory model for identifying significant fibrosis in chronic hepatitis B
    Ting-Ting Wang, Yi-Li Chu, Yi-Qiang Lou, Rou-Yi Yang, Mao-Mao Pu, Lian-Jiang Shan, Lu Huang, Shan-Shan Chen, Hai-Jun Huang
    World Journal of Hepatology.2026;[Epub]     CrossRef
  • Automated analysis of abdominal body composition using MRI: algorithm development and validation via CT comparison
    S.K. Jeon, I. Joo, J.M. Lee, J.-M. Kim, H.-J. Chung, S.J. Park
    Clinical Radiology.2026; 100: 107437.     CrossRef
  • Reply to: “A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B: Is cirrhosis driving the performance?”
    Moon Haeng Hur, Jeong-Hoon Lee
    Journal of Hepatology.2025; 82(3): e143.     CrossRef
  • Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model
    Nanai Xie, Yiwen Liang, Zixin Luo, Jing Hu, Ruiquan Ge, Xiang Wan, Changmiao Wang, Guannan Zou, Feng Guo, Yi Jiang
    Abdominal Radiology.2025; 51(1): 137.     CrossRef
  • Correspondence to editorial on “Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B patients”
    Xiaoqian Xu, Hong You, Jidong Jia, Yuanyuan Kong
    Clinical and Molecular Hepatology.2024; 30(4): 994.     CrossRef
  • Deep learning assisted biomarker development in patients with chronic hepatitis B: Editorial on “Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection”
    Yong Eun Chung
    Clinical and Molecular Hepatology.2024; 30(4): 669.     CrossRef
  • Decreasing performance of HCC prediction models during antiviral therapy for hepatitis B: what else to keep in mind: Editorial on “Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B pati
    Beom Kyung Kim
    Clinical and Molecular Hepatology.2024; 30(4): 656.     CrossRef
  • Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning
    Bingyao Huang, Yi Gao, Lina Wu
    Frontiers in Microbiology.2024;[Epub]     CrossRef
  • 10,976 View
  • 201 Download
  • 11 Web of Science
  • Crossref