Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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
ARTICLE HIGHLIGHTS
Research background

Vessels encapsulating tumor clusters (VETC) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC) and patients with VETC+ HCC show shorter overall survival and disease-free survival and are more prone to progression and metastasis relative to patients with VETC- HCC. So far, VETC is currently determined only on histologic examination after surgical resection.

Research motivation

Preoperative diagnosis of VETC status in HCC is of great significance for predicting the prognosis of HCC patients and determining treatment strategies.

Research objectives

This study aimed to develop and validate a preoperative nomogram based on contrast-enhanced computed tomography (CECT) scanning combined with radiomics and clinical-radiological features to provide a preoperative reference for accurate prediction of VETC status in patients with HCC.

Research methods

This was a retrospective, diagnostic study conducted from January 2017 to March 2023, at two centers. The study included 190 (training set: 106; internal test set: 47; external test set: 37) HCC patients who underwent CECT. Variance threshold, SelectKBest, the least absolute shrinkage and selection operator algorithm and multivariable logistic regression analysis were used to select the useful features and transform them into models. Receiver operating characteristic analysis was employed to compare the identified performance of models in predicting the VETC status of HCC on both training and test sets.

Research results

Among 190 individuals used for radiomics modeling, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66), 94 (49%) were confirmed to have the VETC subtype. The nomogram model included clinical-radiological features and 13 radiomics features and showed good performance for predicting the VETC subtype, with area under the curves of 0.859, 0.848, and 0.757 in the training set, internal test set, and external test set, respectively. The radiomics nomogram outperformed any clinical-radiological feature and the combined radiomics models in terms of clinical predictive abilities, according to a decision curve analysis.

Research conclusions

The findings of this research indicate that a nomogram, developed using clinical-radiological features and combined radiomics features, holds the capability to accurately forecast the VETC status of HCC.

Research perspectives

Our findings may be useful for preoperative identification of VETC subtype in HCC, which could help select HCC patients with poor prognosis, early recurrence, and sorafenib benefit.