109906
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
肝脂肪变性对乙肝相关肝细胞癌术后复发的预测价值：基于机器学习的预后模型的开发
Authors Hao A, Li C, Sun R, Tan B, Shao G, Li K, Li N, Hu W, Qu C, Cao J
Received 26 May 2025
Accepted for publication 22 October 2025
Published 1 November 2025 Volume 2025:12 Pages 2459—2475
DOI https://doi.org/10.2147/JHC.S536629
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Mohamed Shaker
Aoyun Hao, Changlei Li, Ruitao Sun, Bin Tan, Guanming Shao, Kun Li, Na Li, Weiyu Hu, Chao Qu, Jingyu Cao
Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China
Correspondence: Jingyu Cao, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email cjy7027@163.com Chao Qu, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email 2011110479@bjmu.edu.cn
Background: With the increasing prevalence of obesity and type 2 diabetes, the number of patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) coexisting with hepatic steatosis is steadily rising. However, the impact of hepatic steatosis on tumor recurrence following radical resection remains unclear.
Methods: We retrospectively analyzed a cohort of 733 HBV-infected patients diagnosed with HCC who underwent curative liver resection. Propensity score matching (PSM) was performed at a 1:2 ratio using 12 covariates to reduce selection bias and explore the association between preoperative hepatic steatosis and recurrence-free survival (RFS). Furthermore, we constructed a postoperative recurrence prediction model based on hepatic steatosis and other clinicopathological factors using 101 combinations of machine learning algorithms. The optimal model was identified through comprehensive evaluation and validation.
Results: After PSM, survival analysis revealed that patients without hepatic steatosis had significantly better RFS compared to those with steatosis. Multivariate Cox regression analysis confirmed that preoperative hepatic steatosis was an independent risk factor for recurrence following radical resection (P = 0.006, HR:1.564, 95% CI:1.137– 2.150). A recurrence prediction model was developed using hepatic steatosis and additional clinicopathological features through machine learning. Among the 101 models tested, the Random Survival Forest (RSF) model exhibited the best predictive performance, achieving a C-index of 0.719 in the training cohort. The model demonstrated high predictive accuracy for 1-, 2-, and 3-year recurrence, with AUC of 0.782, 0.856, and 0.898, respectively. Compared to conventional staging systems such as BCLC and CNLC, our model achieved superior performance, and decision curve analysis (DCA) demonstrated favorable clinical utility.
Conclusion: Preoperative hepatic steatosis is an independent predictor of recurrence after radical resection in patients with HBV-related HCC. The RSF-based machine learning model incorporating hepatic steatosis and other clinicopathological factors effectively predicts postoperative recurrence risk and may facilitate personalized clinical decision-making in this patient population.
Keywords: hepatocellular carcinoma, recurrence, hepatic steatosis, machine learning
Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China
Correspondence: Jingyu Cao, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email cjy7027@163.com Chao Qu, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong, People’s Republic of China, Email 2011110479@bjmu.edu.cn
Background: With the increasing prevalence of obesity and type 2 diabetes, the number of patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) coexisting with hepatic steatosis is steadily rising. However, the impact of hepatic steatosis on tumor recurrence following radical resection remains unclear.
Methods: We retrospectively analyzed a cohort of 733 HBV-infected patients diagnosed with HCC who underwent curative liver resection. Propensity score matching (PSM) was performed at a 1:2 ratio using 12 covariates to reduce selection bias and explore the association between preoperative hepatic steatosis and recurrence-free survival (RFS). Furthermore, we constructed a postoperative recurrence prediction model based on hepatic steatosis and other clinicopathological factors using 101 combinations of machine learning algorithms. The optimal model was identified through comprehensive evaluation and validation.
Results: After PSM, survival analysis revealed that patients without hepatic steatosis had significantly better RFS compared to those with steatosis. Multivariate Cox regression analysis confirmed that preoperative hepatic steatosis was an independent risk factor for recurrence following radical resection (P = 0.006, HR:1.564, 95% CI:1.137– 2.150). A recurrence prediction model was developed using hepatic steatosis and additional clinicopathological features through machine learning. Among the 101 models tested, the Random Survival Forest (RSF) model exhibited the best predictive performance, achieving a C-index of 0.719 in the training cohort. The model demonstrated high predictive accuracy for 1-, 2-, and 3-year recurrence, with AUC of 0.782, 0.856, and 0.898, respectively. Compared to conventional staging systems such as BCLC and CNLC, our model achieved superior performance, and decision curve analysis (DCA) demonstrated favorable clinical utility.
Conclusion: Preoperative hepatic steatosis is an independent predictor of recurrence after radical resection in patients with HBV-related HCC. The RSF-based machine learning model incorporating hepatic steatosis and other clinicopathological factors effectively predicts postoperative recurrence risk and may facilitate personalized clinical decision-making in this patient population.
Keywords: hepatocellular carcinoma, recurrence, hepatic steatosis, machine learning