Río Bártulos Carolina, Senk Karin, Bade Ragnar, Schumacher Mona, Plath Jan, Kaiser Nico, Wiesinger Isabel, Thurn Sylvia, Stroszczynski Christian, El Mountassir Abdelouahed, Planert Mathis, Woetzel Jan, Wiggermann Philipp
Institut für Röntgendiagnostik und Nuklearmedizin, Städtisches Klinikum Braunschweig gGmbH, 38126 Braunschweig, Germany.
Institut für Röntgendiagnostik, Universitätsklinikum Regensburg, 93053 Regensburg, Germany.
Diagnostics (Basel). 2022 Jul 20;12(7):1750. doi: 10.3390/diagnostics12071750.
In the management of patients with chronic liver disease, the assessment of liver function is essential for treatment planning. Gd-EOB-DTPA-enhanced MRI allows for both the acquisition of anatomical information and regional liver function quantification. The objective of this study was to demonstrate and evaluate the diagnostic performance of two fully automatically generated imaging-based liver function scores that take the whole liver into account. T1 images from the native and hepatobiliary phases and the corresponding T1 maps from 195 patients were analyzed. A novel artificial-intelligence-based software prototype performed image segmentation and registration, calculated the reduction rate of the T1 relaxation time for the whole liver (rrT1) and used it to calculate a personalized liver function score, then generated a unified score-the MELIF score-by combining the liver function score with a patient-specific factor that included weight, height and liver volume. Both scores correlated strongly with the MELD score, which is used as a reference for global liver function. However, MELIF showed a stronger correlation than the rrT1 score. This study demonstrated that the fully automated determination of total liver function, regionally resolved, using MR liver imaging is feasible, providing the opportunity to use the MELIF score as a diagnostic marker in future prospective studies.
在慢性肝病患者的管理中，肝功能评估对于治疗方案的制定至关重要。钆塞酸二钠增强磁共振成像（Gd-EOB-DTPA-enhanced MRI）既能获取解剖学信息，又能对肝脏区域功能进行量化。本研究的目的是展示并评估两种基于成像的、全自动生成的、考虑全肝情况的肝功能评分的诊断性能。分析了195例患者的平扫期和肝胆期T1图像以及相应的T1图。一种新型的基于人工智能的软件原型进行图像分割和配准，计算全肝T1弛豫时间缩短率（rrT1），并用于计算个性化肝功能评分，然后通过将肝功能评分与包括体重、身高和肝脏体积在内的患者特异性因素相结合，生成一个统一的评分——MELIF评分。两种评分均与作为整体肝功能参考的终末期肝病模型（MELD）评分密切相关。然而，MELIF评分的相关性强于rrT1评分。本研究表明，利用磁共振肝脏成像对全肝肝功能进行全自动、区域分辨测定是可行的，为未来前瞻性研究中将MELIF评分用作诊断标志物提供了机会。
