Bartulos, Carolina Rio and Senk, Karin and Bade, Ragnar and Schumacher, Mona and Plath, Jan and Kaiser, Nico and Wiesinger, Isabel and Thurn, Sylvia and Stroszczynski, Christian and El Mountassir, Abdelouahed and Planert, Mathis and Woetzel, Jan and Wiggermann, Philipp (2022) MELIF, a Fully Automated Liver Function Score Calculated from Gd-EOB-DTPA-Enhanced MR Images: Diagnostic Performance vs. the MELD Score. DIAGNOSTICS, 12 (7): 1750. ISSN , 2075-4418
Full text not available from this repository. (Request a copy)Abstract
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(liver)) 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(liver) 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MODEL; VOLUME; FAILURE; DISEASE; liver function; T1 relaxometry; MELIF; MELD; MRI; artificial intelligence |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Röntgendiagnostik Medicine > Zentrum für Neuroradiologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Feb 2024 14:04 |
| Last Modified: | 14 Feb 2024 14:04 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57451 |
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