Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study

Abstract

Yixin Hou,1,* Jianguo Yan,2,* Ke Shi,1,3,* Xiaoli Liu,1 Fangyuan Gao,1 Tong Wu,1 Peipei Meng,1 Min Zhang,2 Yuyong Jiang,1 Xianbo Wang1 1Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People’s Republic of China; 2People’s Liberation Army Fifth Medical Center, Beijing, 100039, People’s Republic of China; 3Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xianbo Wang, Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People’s Republic of China, Email [email protected] Min Zhang, People’s Liberation Army Fifth Medical Center, Beijing, 100039, People’s Republic of China, Email [email protected]: Our objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN).Methods: We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People’s Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People’s Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves.Results: In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670– 0.768), PAGE-B (0. 710) (95% CI 0.660– 0.759), FIB-4 (0.693) (95% CI 0.640– 0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654– 0.756) (p< 0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0– 27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2– 99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6– 60.7), and the NPV was 91.6% (95% CI 89.4– 93.4). These findings were validated in the independent validation cohort.Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC.Keywords: machine learning-based model, hepatocellular carcinoma, risk, hepatitis B-related cirrhosis

Keywords

• machine learning-based model
• hepatocellular carcinoma
• risk
• hepatitis b-related cirrhosis