Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Sep 15, 2024; 16(9): 3839-3850
Published online Sep 15, 2024. doi: 10.4251/wjgo.v16.i9.3839
Construction and evaluation of a liver cancer risk prediction model based on machine learning
Ying-Ying Wang, Wan-Xia Yang, Qia-Jun Du, Zhen-Hua Liu, Ming-Hua Lu, Chong-Ge You
Ying-Ying Wang, Wan-Xia Yang, Qia-Jun Du, Zhen-Hua Liu, Ming-Hua Lu, Chong-Ge You, Laboratory Medicine Center, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, Gansu Province, China
Co-first authors: Ying-Ying Wang and Wan-Xia Yang.
Author contributions: Wang YY and Yang WX served as co-first authors, conceiving and designing the study; Liu ZH, Lu MH, and Du QJ collected the research data; You CG supervised the entire study and revised the manuscript; All authors contributed to the article and approved the submitted version.
Supported by the Cuiying Scientific and Technological Innovation Program of the Second Hospital, No. CY2021-BJ-A16 and No. CY2022-QN-A18; and Clinical Medical School of Lanzhou University and Lanzhou Science and Technology Development Guidance Plan Project, No. 2023-ZD-85.
Institutional review board statement: The study was reviewed and approved for publication by the authors’ Institutional Review Board (Medical Ethics Committee of The Second Hospital & Clinical Medical School, Lanzhou University, China; No. 2024A-075).
Informed consent statement: Informed consent was obtained from all individuals included in this study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original anonymous data set is available on request from the corresponding author at youchg@lzu.edu.cn.
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: Chong-Ge You, PhD, Chief, Laboratory Medicine Center, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou 730030, Gansu Province, China. youchg@lzu.edu.cn
Received: March 29, 2024
Revised: July 31, 2024
Accepted: August 7, 2024
Published online: September 15, 2024
Processing time: 163 Days and 23.7 Hours
Abstract
BACKGROUND

Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.

AIM

To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance.

METHODS

In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk.

RESULTS

Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.

CONCLUSION

The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Keywords: Hepatocellular carcinoma; Cirrhosis; Prediction model; Machine learning; Random forest

Core Tip: We constructed a prediction model for hepatocellular carcinoma with reliable and effective clinical diagnostic capacity. In the training cohort (n = 385), machine learning models were developed based on six variables including age; white blood cell, red blood cell, and platelet counts; and alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels. The performance of these models was assessed in an independent validation cohort of 165 subjects. We compared our model with the ASAP model using receiver operating characteristic curve, calibration, and decision curve analysis. Our findings demonstrated that a random forest model exhibited discriminatory power, calibration performance, and clinical utility.