Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 7, 2021; 27(17): 2015-2024
Published online May 7, 2021. doi: 10.3748/wjg.v27.i17.2015
Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics
Peng Liu, Xian-Zhen Tan, Ting Zhang, Qian-Biao Gu, Xian-Hai Mao, Yan-Chun Li, Ya-Qiong He
Peng Liu, Xian-Zhen Tan, Qian-Biao Gu, Ya-Qiong He, Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, Hunan Province, China
Ting Zhang, Department of Radiology, Hunan Children's Hospital, Changsha 410000, Hunan Province, China
Xian-Hai Mao, Department of Hepatological Surgery, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, Hunan Province, China
Yan-Chun Li, Department of Pathology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha 410005, Hunan Province, China
Author contributions: Mao XH and Li YC contributed equally to this work; Liu P designed the research study; Zhang T and Tan XZ performed the research; Gu QB contributed new reagents and analytic tools; Liu P, Zhang T, and He YQ analyzed the data and wrote the manuscript; all authors have read and approved the final manuscript.
Supported by Scientific Research Program of Hunan Provincial Health Commission, China, No. B2019072; and Changsha Science and Technology Project, China, No. kq1907062.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University).
Informed consent statement: All patients provided written informed consent.
Conflict-of-interest statement: We have no financial relationships to disclose.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ya-Qiong He, MD, Associate Professor, Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), No. 61 Jiefang West Road, Changsha 410005, Hunan Province, China. 641474988@qq.com
Received: January 18, 2021
Peer-review started: January 18, 2021
First decision: February 9, 2021
Revised: February 22, 2021
Accepted: March 31, 2021
Article in press: March 31, 2021
Published online: May 7, 2021
ARTICLE HIGHLIGHTS
Research background

Liver cancer is one of the most common malignant tumors, and ranks as the fourth leading cause of cancer death worldwide. Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement, and evaluation and better prediction.

Research motivation

At present, few studies have focused on the prediction of MVI in the early stage of hepatocellular carcinoma (HCC) (which refers to solitary tumor with a size of ≤ 5 cm, without MVI). Our study aimed to investigate the predictive value of computed tomography (CT) radiomics for MVI in solitary HCC ≤ 5 cm.

Research objectives

This study aimed to investigate the predictive value of radiomics for MVI in solitary HCC ≤ 5 cm.

Research methods

A total of 185 HCC patients, including 122 MVI negative and 63 MVI positive patients, were retrospectively analyzed. All patients were randomly assigned to the training group (n = 124) and validation group (n = 61), at a ratio of 2:1. A total of 1351 radiomic features were extracted based on three-dimensional images. In the training group, the least absolute shrinkage and selection operator feature selection algorithm was used to reduce the dimensions, and the most relevant radiomic features of MVI were selected to calculate the image score (Rad-score, RS) of each patient. The diagnostic performance of the radiomics model was verified in the validation group, and the Delong test was applied to compare the radiomics and MVI-related imaging features (two-trait predictor of venous invasion and radiogenomic invasion).

Research results

A total of ten radiomics features were finally obtained after screening 1531 features. According to the weighting coefficient that corresponded to the features, the RS calculation formula was obtained, and the RS score of each patient was calculated. The radiomics model exhibited a better correction and identification ability in the training and validation groups [area under the curve: 0.72 (95% confidence interval: 0.58-0.86) and 0.74 (95% confidence interval: 0.66-0.83), respectively]. Its prediction performance was significantly higher than that of the image features (P < 0.05).

Research conclusions

CT radiomics has certain predictive value for MVI in solitary HCC ≤ 5 cm, and the predictive ability is higher than that of image features.

Research perspectives

The accurate prediction of MVI before surgery is desperately needed. Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement and evaluation, and better prediction. At present, few studies have focused on the prediction of MVI in the early stage of HCC (which refers to solitary tumor with a size of ≤ 5 cm, without MVI). The present study aimed to investigate the predictive value of CT radiomics for MVI in solitary HCC ≤ 5 cm.