慢性乙型肝炎 (CHB) 患者临床结果的预测对于有效治疗至关重要。本研究旨在评估使用深度学习算法进行计算机断层扫描 (CT) 分析对 CHB 患者的预后价值。这项回顾性研究纳入了 2,169 名无肝代偿失调的 CHB 患者，这些患者在2005 年 1 月和 2016 年 6 月。使用基于深度学习的全自动器官分割算法从 CT 图像中获取肝脏和脾脏体积以及身体成分测量值，包括皮下脂肪组织 (SAT)、内脏脂肪组织 (VAT) 和骨骼肌指数。我们使用 Cox 比例风险分析评估了 HCC、肝功能失代偿、糖尿病 (DM) 和总生存期 (OS) 的重要预测因素。在 103.0 个月的中位随访期内，HCC (n=134, 6.2%)、发生肝功能失代偿（n=103，4.7%）、糖尿病（n=432，19.9%）和死亡（n=120，5.5%）。根据多变量分析，标准化脾脏体积以及年龄、性别、白蛋白和血小板计数可显着预测 HCC 的发展（风险比 [HR]=1.01，P=0.025）。标准化脾体积（HR=1.01，P<0.001）和VAT指数（HR=0.98，P=0.004）与年龄和白蛋白等肝功能失代偿显着相关。此外，VAT 指数（HR=1.01，P=0.001）和标准化脾脏体积（HR=1.01，P=0.001）以及性别、年龄和白蛋白是 DM 的重要预测因子。 SAT 指数（HR=0.99，P=0.004）与 OS、年龄、白蛋白和 MELD 显着相关。基于深度学习的自动测量脾脏体积、VAT 和 SAT 指数可以为 CHB 患者提供各种预后信息。

The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB.This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses.During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD.Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.