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
Processing time: 100 Days and 17.8 Hours
Abstract
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. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). 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.

AIM

To investigate the predictive value of computed tomography radiomics for MVI in solitary HCC ≤ 5 cm.

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). A total of 1351 radiomic features were extracted based on three-dimensional images. 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).

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 radiomics score (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).

CONCLUSION

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

Keywords: Hepatocellular carcinoma; Microvascular invasion; Radiomics; Image features; Computed tomography

Core Tip: Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). This study showed that radiomics as an emerging method at present had a good diagnostic efficiency and exhibited better accuracy in predicting MVI than image features, indicating that radiomics is a more suitable method in predicting MVI in solitary HCC ≤ 5 cm.