Cheng CH, Hao WR, Cheng TH. Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis. World J Clin Cases 2025; 13(4): 98550 [DOI: 10.12998/wjcc.v13.i4.98550]
Corresponding Author of This Article
Tzu-Hurng Cheng, PhD, Professor, Department of Biochemistry, School of Medicine, College of Medicine, China Medical University, No. 91 Xueshi Road, North District, Taichung 404333, Taiwan. thcheng@mail.cmu.edu.tw
Research Domain of This Article
Imaging Science & Photographic Technology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Feb 6, 2025; 13(4): 98550 Published online Feb 6, 2025. doi: 10.12998/wjcc.v13.i4.98550
Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis
Chun-Han Cheng, Wen-Rui Hao, Tzu-Hurng Cheng
Chun-Han Cheng, Department of Medical Education, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
Wen-Rui Hao, Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Ministry of Health and Welfare, Taipei Medical University, New Taipei 23561, Taiwan
Wen-Rui Hao, Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11002, Taiwan
Tzu-Hurng Cheng, Department of Biochemistry, School of Medicine, College of Medicine, China Medical University, Taichung 404333, Taiwan
Co-first authors: Chun-Han Cheng and Wen-Rui Hao.
Author contributions: Cheng CH and Hao WR wrote the paper; Cheng TH revised the paper; All authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tzu-Hurng Cheng, PhD, Professor, Department of Biochemistry, School of Medicine, College of Medicine, China Medical University, No. 91 Xueshi Road, North District, Taichung 404333, Taiwan. thcheng@mail.cmu.edu.tw
Received: June 28, 2024 Revised: October 21, 2024 Accepted: October 29, 2024 Published online: February 6, 2025 Processing time: 139 Days and 2.9 Hours
Abstract
This editorial examines a recent study that used radiomics based on computed tomography (CT) to predict the expression of the fibroblast-related gene enhancer of zeste homolog 2 (EZH2) and its correlation with the survival of patients with hepatocellular carcinoma (HCC). By integrating radiomics with molecular analysis, the study presented a strategy for accurately predicting the expression of EZH2 from CT scans. The findings demonstrated a strong link between the radiomics model, EZH2 expression, and patient prognosis. This noninvasive approach provides valuable insights into the therapeutic management of HCC.
Core Tip: This editorial discusses a study that combined radiomics based on computed tomography (CT) with molecular analysis for predicting the expression of the gene enhancer of zeste homolog 2 (EZH2) in patients with hepatocellular carcinoma (HCC). The radiomics model revealed a strong correlation with both EZH2 expression and patient survival, providing a noninvasive approach for obtaining key molecular insights. This innovative approach may enhance the prediction of HCC prognosis and may guide the implementation of personalized treatment, marking a major advancement in cancer diagnostics and precision medicine.
