Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
CHESS Center, Institute of Portal Hypertension, First Hospital of Lanzhou University, Lanzhou, China.
Cell Rep Med. 2022 Mar 15;3(3):100563. doi: 10.1016/j.xcrm.2022.100563.
The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (HVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final HVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.
肝静脉压力梯度（HVPG）是肝硬化门静脉高压（PHT）的金标准，但它具有侵袭性和专业性。需要替代的非侵入性技术来评估肝静脉压力梯度（HVPG）。在这里，我们开发了一种自动机器学习 CT 放射组学 HVPG 定量模型（HVPG），然后通过 HVPG 分期（≥10、≥12、≥16 和≥20mmHg）的接收者操作特征曲线（AUC）在内部和外部测试数据集验证该模型，并将该模型与基于影像学和血清学的工具进行比较。最终的 HVPG 模型获得了超过 0.80 的 AUC，并且优于其他用于评估 HVPG 的非侵入性工具。该模型在识别 PHT 严重程度方面表现出性能的提升，这可能有助于在无法进行经颈静脉 HVPG 测量时进行非侵入性 HVPG 初级预防。
