Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 21, 2022; 28(31): 4376-4389
Published online Aug 21, 2022. doi: 10.3748/wjg.v28.i31.4376
Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma
Peng-Zhan Deng, Bi-Geng Zhao, Xian-Hui Huang, Ting-Feng Xu, Zi-Jun Chen, Qiu-Feng Wei, Xiao-Yi Liu, Yu-Qi Guo, Sheng-Guang Yuan, Wei-Jia Liao
Peng-Zhan Deng, Bi-Geng Zhao, Xian-Hui Huang, Ting-Feng Xu, Zi-Jun Chen, Qiu-Feng Wei, Xiao-Yi Liu, Yu-Qi Guo, Sheng-Guang Yuan, Wei-Jia Liao, Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
Author contributions: Deng PZ, Zhao BG and Huang XH contributed equally to this work; Liao WJ was the guarantor and designed the study; Deng PZ, Zhao BG, Xu TF, Chen ZJ, Wei QF, and Liu XY participated in the acquisition, analysis, and interpretation of the data; Deng PZ and Huang XH drafted the initial manuscript; Guo YQ, Yuan SG, and Liao WJ revised the article critically for important intellectual content.
Supported by the National Natural Science Foundation of China, No. 81372163; the Science and Technology Planning Project of Guilin, No. 20190218-1; the Openin Project of Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education, No. GKE-KF202101; the Program of Guangxi Zhuang Autonomous Region health and Family Planning Commission, No. Z20210706; and the Innovation and Entrepreneurship Project of University Students in Guangxi, No. 202110601002.
Institutional review board statement: The study was reviewed and approved by the research ethics committee of Affiliated Hospital of Guilin Medical University (Approval NO. 2021WJWZC14).
Informed consent statement: Informed consent was obtained from all patients.
Conflict-of-interest statement: There are no conflicts of interest to report.
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: Wei-Jia Liao, MD, Chief Doctor, Professor, Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, No. 15 Lequn Road, Xiufeng District, Guilin 541001, Guangxi Zhuang Autonomous Region, China. liaoweijia288@163.com
Received: April 20, 2022
Peer-review started: April 20, 2022
First decision: June 2, 2022
Revised: June 14, 2022
Accepted: July 20, 2022
Article in press: July 20, 2022
Published online: August 21, 2022
Processing time: 118 Days and 2.8 Hours
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.

AIM

To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.

METHODS

A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.

RESULTS

In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).

CONCLUSION

The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

Keywords: Hepatocellular carcinoma; Radiomics; Contrast-enhanced computed tomography; Survival prediction

Core Tip: The prognosis of hepatocellular carcinoma (HCC) patients remains poor even after radical resection. Therefore, a precise and reliable tool to predict the prognosis of HCC patients is urgently needed. We established a predictive model incorporating radiomics features extracted from preoperative contrast-enhanced computed tomography images, alpha-fetoprotein and neutrophil-to-lymphocyte ratio to predict the overall survival of patients with HCC, and the model was visualized via a nomogram. The nomogram showed good accuracy for survival prediction.