Heo Subin, Jeong Boryeong, Lee Seung Soo, Kim Minju, Jang Hyeon Ji, Choi Se Jin, Kim Kang Mo, Ha Tae-Yong, Jung Dong-Hwan
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eur Radiol. 2025 Feb 14. doi: 10.1007/s00330-025-11411-9.
While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aimed to evaluate the impact of CT-based CSPH on outcomes following hepatectomy for hepatocellular carcinoma (HCC).
This retrospective single-center study included patients with advanced chronic liver disease (ACLD) who underwent hepatectomy for very early or early-stage HCC between January 2017 and December 2018. CSPH was assessed using CT-based criteria, which included splenomegaly determined by deep learning-based spleen volume measurements with personalized reference thresholds, and the presence of gastroesophageal varices (GEV), spontaneous portosystemic shunt or ascites. Logistic regression and competing risk analyses were used to identify factors associated with severe post-hepatectomy liver failure (PHLF), hepatic decompensation, and liver-related death or transplantation. The predictive performance of existing models for PHLF was compared using both CT-based and conventional CSPH criteria (endoscopic GEV or splenomegaly with thrombocytopenia).
Among 593 patients (460 men; mean age 57.9 ± 9.3 years), 41 (6.9%) developed severe PHLF. The median follow-up period was 62 months. CT-based CSPH independently predicted severe PHLF (OR 7.672 [95% CI 3.209-18.346]), hepatic decompensation (subdistribution hazard ratio (sHR) 4.518 [1.868-10.929]), and liver-related death or transplantation (sHR 2.756 [1.315-5.773]). When integrated into existing models, CT-based CSPH outperformed conventional CSPH in predicting severe PHLF (AUC 0.724 vs. 0.694 for EASL algorithm (p = 0.036) and 0.854 vs. 0.830 for Wang's model (p = 0.011)).
CT-based CSPH is a strong predictor of poor post-hepatectomy outcomes in HCC patients with ACLD, offering a noninvasive surgical risk assessment tool.
Question Can CT-based detection of clinically significant portal hypertension (CSPH) serve as a noninvasive predictor of post-hepatectomy outcomes in hepatocellular carcinoma (HCC) patients? Findings CT-based CSPH independently predicted severe post-hepatectomy liver failure, hepatic decompensation, and liver-related death or transplantation, outperforming conventional CSPH criteria in predictive performance. Clinical relevance CT-based CSPH offers a noninvasive and effective tool for surgical risk assessment in HCC patients, potentially improving the selection of candidates for hepatectomy and optimizing patient outcomes.
虽然基于CT检测临床显著门静脉高压（CSPH）的方法已成为评估CSPH的一种非侵入性替代方法，但其对肝切除术后结局的预测能力尚不清楚。因此，本研究旨在评估基于CT的CSPH对肝细胞癌（HCC）肝切除术后结局的影响。
这项回顾性单中心研究纳入了2017年1月至2018年12月期间因极早期或早期HCC接受肝切除术的晚期慢性肝病（ACLD）患者。使用基于CT的标准评估CSPH，该标准包括通过基于深度学习的脾脏体积测量和个性化参考阈值确定的脾肿大，以及食管胃静脉曲张（GEV）、自发性门体分流或腹水的存在。采用逻辑回归和竞争风险分析来确定与严重肝切除术后肝功能衰竭（PHLF）、肝失代偿以及肝相关死亡或移植相关的因素。使用基于CT的和传统的CSPH标准（内镜下GEV或伴有血小板减少的脾肿大）比较现有PHLF模型的预测性能。
在593例患者（460例男性；平均年龄57.9±9.3岁）中，41例（6.9%）发生了严重PHLF。中位随访期为62个月。基于CT的CSPH独立预测严重PHLF（OR 7.672[95%CI 3.209 - 18.346]）、肝失代偿（亚分布风险比（sHR）4.518[1.868 - 10.929]）以及肝相关死亡或移植（sHR 2.756[1.315 - 5.773]）。当纳入现有模型时，基于CT的CSPH在预测严重PHLF方面优于传统CSPH（EASL算法的AUC为0.724对0.694（p = 0.036），Wang模型的AUC为0.854对0.830（p = 0.011））。
基于CT的CSPH是ACLD的HCC患者肝切除术后不良结局的有力预测指标，提供了一种非侵入性手术风险评估工具。
问题基于CT检测临床显著门静脉高压（CSPH）能否作为肝细胞癌（HCC）患者肝切除术后结局的非侵入性预测指标？发现基于CT的CSPH独立预测严重肝切除术后肝功能衰竭、肝失代偿以及肝相关死亡或移植，在预测性能上优于传统CSPH标准。临床意义基于CT的CSPH为HCC患者的手术风险评估提供了一种非侵入性且有效的工具，可能改善肝切除候选者的选择并优化患者结局。
