Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2024; 16(5): 1808-1820
Published online May 15, 2024. doi: 10.4251/wjgo.v16.i5.1808
Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging
Hui-Lin Chen, Rui-Lin He, Meng-Ting Gu, Xing-Yu Zhao, Kai-Rong Song, Wen-Jie Zou, Ning-Yang Jia, Wan-Min Liu
Hui-Lin Chen, Rui-Lin He, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Hui-Lin Chen, Kai-Rong Song, Ning-Yang Jia, Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
Meng-Ting Gu, Xing-Yu Zhao, Wen-Jie Zou, Wan-Min Liu, Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
Co-first authors: Hui-Lin Chen and Rui-Lin He.
Co-corresponding authors: Ning-Yang Jia and Wan-Min Liu.
Author contributions: Chen HL and He RL contributed equally to this work and share first authorship; Jia NY and Liu WM contributed equally to this work and should be considered as co-corresponding authors; Liu WM and Jia NY designed this study; Chen HL, Liu WM, Gu MT, He RL, Zhao XY, Song KR, and Zou WJ performed the primary literature and data collection; Chen HL and Liu WM analyzed the data and wrote the manuscript; Jia NY and Liu WM were responsible for revising the manuscript for important intellectual content; all authors read and approved the final version.
Supported by the Project of Shanghai Municipal Commission of Health, No. 2022LJ024.
Institutional review board statement: The studies involving human participants were reviewed and approved by the Third Affiliated Hospital of Shanghai Naval Military Medical University and Tongji University Affiliated Tongji Hospital.
Informed consent statement: Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data sharing statement: No additional data are available.
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: Ning-Yang Jia, PhD, Doctor, Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, No. 225 Changhai Road, Yangpu District, Shanghai 200438, China. ningyangjia@163.com
Received: November 10, 2023
Peer-review started: November 10, 2023
First decision: January 30, 2024
Revised: February 2, 2024
Accepted: March 12, 2024
Article in press: March 12, 2024
Published online: May 15, 2024
Processing time: 181 Days and 10.3 Hours
ARTICLE HIGHLIGHTS
Research background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, and HCC patients have a poor prognosis. Vessels encapsulating tumor clusters (VETC) are a vascular pattern associated with a novel metastasis mechanism and have been proven to be an independent poor prognostic factor for early HCC patients.

Research motivation

It seems that no one has focused on predicting the VETC pattern of small HCC (sHCC; ≤ 3 cm) patients in multicenter studies.

Research objectives

To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.

Research methods

We collected patients with VETC and HCC status from three hospitals. Data from one hospital were used as a training set to train the prediction model, while those from the other two hospitals were used as test and validation sets, respectively. Univariate and multivariate logistic regression analyses were used to screen the independent predictive factors associated with VETC, and these factors were included to construct a model for predicting the pattern of VETC in sHCC patients. The performance of the model was evaluated using area under curve (AUC), decision curve analysis (DCA), and calibration curve. Kaplan-Meier survival analysis was performed to confirm whether the VETC status predicted by the model was associated with early recurrence in sHCC patients.

Research results

The independent predictive factors that we identified include alpha-fetoprotein_lg10, carbohydrate antigen 199 (CA199), irregular shape, non-smooth margin, and arterial peritumoral enhancement. The model for predicting VETC status, which incorporates these factors, showed good results under the evaluation of AUC, DCA, and calibration curves in the three sets. Finally, Kaplan-Meier survival analysis confirmed that the VETC pattern was associated with early recurrence in sHCC patients.

Research conclusions

The nomogram constructed by incorporating preoperative clinical parameters and imaging features has undergone extensive validation across multiple patient centers, demonstrating strong predictive performance and having good significance for predicting postoperative recurrence.

Research perspectives

Improving the predictive performance of VETC status in preoperative prediction of sHCC patients also requires the combination of radiomics and artificial intelligence, in order to better provide assistance for clinical treatment decision-making.