Observational Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Aug 15, 2023; 15(8): 1486-1496
Published online Aug 15, 2023. doi: 10.4251/wjgo.v15.i8.1486
Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes
Zhi-Jie Liu, Yue Xu, Wen-Xuan Wang, Bin Guo, Guo-Yuan Zhang, Guang-Cheng Luo, Qiang Wang
Zhi-Jie Liu, Department of Clinical Transfusion, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Yue Xu, Bin Guo, Guo-Yuan Zhang, Guang-Cheng Luo, Qiang Wang, Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Wen-Xuan Wang, Department of Radiology, Nanchong Central Hospital, Nanchong 637000, Sichuan Province, China
Author contributions: Luo GC and Wang Q contributed to the conceptualisation and design of this study; Liu ZJ, Xu Y, Wang WX, Guo B, and Zhang GY contributed to the clinical data collection and analysis; Liu ZJ, Xu Y, and Wang WX prepared and wrote the first draft of this manuscript; Wang Q revised the manuscript.
Institutional review board statement: This study was approved by the Ethics Committee of Affiliated Hospital of North Sichuan Medical College and conducted in accordance with the declaration of Helsinki Principles.
Informed consent statement: The informed consent of this study was exempted by the Ethics Committee of Affiliated Hospital of North Sichuan Medical College.
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 the corresponding author at email address. Participants gave informed consent for data sharing.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
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: Qiang Wang, PhD, Research Assistant Professor, Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong 637000, Sichuan Province, China. wqiang_1981@126.com
Received: February 10, 2023
Peer-review started: February 10, 2023
First decision: May 19, 2023
Revised: May 28, 2023
Accepted: June 25, 2023
Article in press: June 25, 2023
Published online: August 15, 2023
ARTICLE HIGHLIGHTS
Research background

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, which currently faces difficulties in early diagnosis, high recurrence rate, and low overall survival rate. Early detection and diagnosis are main way to reduce the incidence rate and mortality of HCC.

Research motivation

Using logistic regression models to identify high-risk factors related to HCC, and combining clinical features and liver related indicators to establish a predictive model for HCC.

Research objectives

This study aims to establish a model that can predict HCC and can be applied in clinical practice.

Research methods

Patients were divided into a modeling group and a validation group based on the results of puncture biopsy or surgical pathological diagnosis. HCC was used as the dependent variable, and the research indicators were included in logistic univariate and multivariate analysis to establish a HCC risk prediction model.

Research results

Logistic univariate analysis showed that, gender, age, alpha-fetoprotein (AFP), and protein induced by vitamin K absence or antagonist-II (PIVKA-II), gamma-glutamyl transferase (GGT), aspartate transaminase (AST), hepatitis B surface antigen (HBsAg) were risk factors for HCC, and in the training cohort and confirming with the validation cohort, the NSMC-HCC model has good sensitivity and specificity in high-risk populations with HCC, with a high accuracy in early-stage HCC diagnosis.

Research conclusions

We have established a relatively effective HCC risk prediction model that includes gender, age, AFP, PIVKA-I, total bilirubin, GGT, AST, alanine amino transferase, total bile acid, and HBsAg, and this model has high accuracy in the diagnosis of early HCC.

Research perspectives

This study is an observational study that included samples from the same medical institution, which may have sampling bias. Further validation of multicenter, large sample studies is needed in the future.