本研究旨在评估从深度学习分析获得的定量指标在预测晚期慢性肝病（ACLD）患者的失调和死亡方面的实用性，以及它们的纵向变化。我们纳入了具有前瞻性队列的患者，他们在三级医疗中心的肝细胞癌筛查期间接受了增强剂哌醇酸MRI，包括基线和1年随访MRI。基线肝脏情况被归类为非ACLD、补偿性ACLD和失调性ACLD。在HBP影像上，利用深度学习算法自动测量肝-脾信号强度比（LS-SIR）和肝-脾体积比（LS-VR），并计算其1年随访时的百分比变化（∆LS-SIR和∆LS-VR）。使用多变量Fine和Gray回归模型进行了竞争风险分析，评估MRI指标与肝失调和肝相关死亡或移植的关联性，包括仅基线参数和基线和随访参数。 我们的研究纳入了280名患者（153名男性，平均年龄±标准差为57±7.95岁），其中32名是非ACLD，186名是补偿性ACLD，62名是失调性ACLD。患者随访时间为11-117个月（中位数为104个月）。在补偿性ACLD患者中，基线LS-SIR（亚分布风险比[sHR]，0.81；p=0.034）和LS-VR（sHR，0.71；p=0.01）独立与肝失调相关。在调整基线变量后，ΔLS-VR（sHR，0.54；p=0.002）预测肝失调。ΔLS-VR是补偿性ACLD患者（sHR，0.46；p=0.026）和失调性ACLD患者（sHR，0.61；p=0.023）的独立肝相关死亡或移植的预测因子。从哌醇酸增强HBP MRI的深度学习分析自动获得的MRI指标可用作ACLD患者的预后标记。 版权所有 © 2022年韩国放射学会。

This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD).We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters.Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023).MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.Copyright © 2022 The Korean Society of Radiology.