Zhang C, Zhong H, Zhao F, Ma ZY, Dai ZJ, Pang GD. Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography. World J Gastrointest Oncol 2024; 16(3): 857-874 [PMID: 38577448 DOI: 10.4251/wjgo.v16.i3.857]
Corresponding Author of This Article
Guo-Dong Pang, MD, PhD, Associate Chief Physician, Doctor, Department of Radiology, The Second Hospital of Shandong University, No. 247 Beiyuan Road, Tianqiao District, Jinan 250033, Shandong Province, China. pgd226@aliyun.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
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 Gastrointest Oncol. Mar 15, 2024; 16(3): 857-874 Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.857
Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography
Chao Zhang, Hai Zhong, Fang Zhao, Zhen-Yu Ma, Zheng-Jun Dai, Guo-Dong Pang
Chao Zhang, Hai Zhong, Guo-Dong Pang, Department of Radiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
Fang Zhao, Department of Radiology, Qilu Hospital of Shandong University, Jinan 250014, Shandong Province, China
Zhen-Yu Ma, Department of Radiology, Linglong Yingcheng Hospital, Yantai 265499, Shandong Province, China
Zheng-Jun Dai, Department of Scientific Research, Huiying Medical Technology Co., Ltd, Beijing 100192, China
Co-first authors: Chao Zhang and Hai Zhong.
Author contributions: Zhang C, Zhong H, and Pang GD designed the research study, analyzed the data, and wrote the manuscript; Zhao F and Ma ZY performed the research; Dai ZJ contributed new reagents and analytic tools; and all authors have read and approve the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Second Hospital of Shandong University Institutional Review Board, IRB No. KYLL-2023LW044.
Informed consent statement: The requirement for informed consent was waived because of the retrospective data sets.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code and dataset available from corresponding author at pgd226@aliyun.com.
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: Guo-Dong Pang, MD, PhD, Associate Chief Physician, Doctor, Department of Radiology, The Second Hospital of Shandong University, No. 247 Beiyuan Road, Tianqiao District, Jinan 250033, Shandong Province, China. pgd226@aliyun.com
Received: October 30, 2023 Peer-review started: October 30, 2023 First decision: December 21, 2023 Revised: December 26, 2023 Accepted: January 29, 2024 Article in press: January 29, 2024 Published online: March 15, 2024 Processing time: 134 Days and 7.4 Hours
Core Tip
Core Tip: Vessels encapsulating tumor clusters (VETC) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC) and currently determined only on histologic examination after surgical resection. We evaluated 190 patients with pathologically confirmed HCC and constructed a machine learning-based contrast-enhanced computed tomography radiomics model, performed canonical screening of features and multiple validations, and confirmed robustness on various data resources. The radiomics model showed remarkable performance in predicting the VETC subtype, and the results were reproducible, demonstrating that the approach may be applied to other patient samples. Radiomics could provide valuable information for assisting clinicians in pretreatment decision-making.
