Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 21, 2023; 29(43): 5804-5817
Published online Nov 21, 2023. doi: 10.3748/wjg.v29.i43.5804
Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma
Yu-Bo Zhang, Gang Yang, Yang Bu, Peng Lei, Wei Zhang, Dan-Yang Zhang
Yu-Bo Zhang, Gang Yang, Peng Lei, Wei Zhang, Dan-Yang Zhang, Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
Yang Bu, Department of Hepatobiliary Surgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan 750003, Ningxia Hui Autonomous Region, China
Author contributions: Zhang YB analyzed the data and wrote the manuscript; Yang G analyzed the data and wrote the original draft; Bu Y contributed the resources; Lei P designed the research; Zhang W analyzed the data; Zhang DY wrote the original draft.
Supported by Ningxia Key Research and Development Program, No. 2018BEG03001.
Institutional review board statement: The study was approved by the Ethics Committee of the General Hospital of Ningxia Medical University (KYLL-2023-0378).
Informed consent statement: Written informed consent was provided by individual patients.
Conflict-of-interest statement: The authors declare no conflict of interest for this article.
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: Peng Lei, MD, Doctor, Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, No. 84 Shenli Nan Road, Yinchuan 750003, Ningxia Hui Autonomous Region, China. leipengnx@126.com
Received: August 26, 2023
Peer-review started: August 26, 2023
First decision: September 18, 2023
Revised: October 7, 2023
Accepted: November 3, 2023
Article in press: November 3, 2023
Published online: November 21, 2023
Processing time: 85 Days and 17.3 Hours
ARTICLE HIGHLIGHTS
Research background

Surgical resection is still the main treatment for hepatocellular carcinoma (HCC). HCC recurrence is the main factor affecting patients' survival rate after surgery. Developing pre-operative non-invasive predictive methods will be highly significant in identifying patients at high risk of postoperative recurrence and precise management of those patients by closely monitoring and individualized treatment on time.

Research motivation

To develop a new risk prediction model for the early postoperative recurrence of HCC and enhance the feasibility and applicability of the constructed model.

Research objectives

This study aimed to identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.

Research methods

The demographic and clinical data of 371 HCC patients were collected and analyzed, and the key feature variables were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.

Research results

Following machine learning analysis, eight key feature variables were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 [95% confidence interval (95%CI): 0.982-1.000], 0.734 (95%CI: 0.601-0.867), and 0.706 (95%CI: 0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.

Research conclusions

The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Research perspectives

A multicenter study with large samples should be conducted in the future, and comparing our model with other prediction models is needed to further verify its reliability.