MELIF, a Fully Automated Liver Function Score Calculated from Gd-EOB-DTPA-Enhanced MR Images: Diagnostic Performance vs. the MELD Score

Bártulos, Carolina Río; Senk, Karin; Bade, Ragnar; Schumacher, Mona; Plath, Jan; Kaiser, Nico; Wiesinger, Isabel; Thurn, Sylvia; Stroszczynski, Christian; Mountassir, Abdelouahed El; Planert, M.; Woetzel, Jan; Wiggermann, Philipp

doi:10.3390/diagnostics12071750

Cited by 1 publication

(3 citation statements)

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“…Compared to the classic rrT1, which is based on manual measurements of individual regions of interest, the MELIF score is calculated fully automatically for the entire liver and takes into account several patient-specific factors such as size, weight and liver volume. In the first retrospective study to investigate this matter, the MELIF score was shown to perform better than the whole liver rrT1 score based on the MELD score as a reference for liver function 25 . In this study, the MELIF score correlated strongly with surrogate parameters of liver function, such as the MELD, CP, and ALBI scores, and it demonstrated the strongest correlation with the ALBI score (r = − 0.65).…”

Section: Discussionmentioning

confidence: 99%

“…The MELIF score was calculated fully automatically using prototype software, with Eq. ( 3 ) describing the previously published calculation methodology 25 . The equation contains patient-specific factors (p) such as height, weight and liver volume.…”

Section: Methodsmentioning

confidence: 99%

“…With the help of artificial intelligence (AI) and deep learning, it has become possible to develop software that fully automatically calculates a liver function score based on the rrT1, the so-called MELIF score. The MELIF score considers the rrT1 value of the entire liver instead of the values of individual regions of interest and incorporates other patient-specific factors such as height, weight and liver volume 25 . This provides a tool that can automatically determine regional liver function from imaging data, and in a first retrospective analysis, the MELIF score was shown to correlate better with the MELD score than the rrT1 score of the whole liver 25 .…”

Section: Introductionmentioning

confidence: 99%

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Using AI and Gd-EOB-DTPA-enhanced MR imaging to assess liver function, comparing the MELIF score with the ALBI score

Río Bártulos,

Senk,

Bade

et al. 2023

Sci Rep

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Monitoring disease progression is particularly important for determining the optimal treatment strategy in patients with liver disease. Especially for patients with diseases that have a reversible course, there is a lack of suitable tools for monitoring liver function. The development and establishment of such tools is very important, especially in view of the expected increase in such diseases in the future. Image-based liver function parameters, such as the T1 relaxometry-based MELIF score, are ideally suited for this purpose. The determination of this new liver function score is fully automated by software developed with AI technology. In this study, the MELIF score is compared with the widely used ALBI score. The ALBI score was used as a benchmark, as it has been shown to better capture the progression of less severe liver disease than the MELD and Child‒Pugh scores. In this study, we retrospectively determined the ALBI and MELIF scores for 150 patients, compared these scores with the corresponding MELD and Child‒Pugh scores (Pearson correlation), and examined the ability of these scores to discriminate between good and impaired liver function (AUC: MELIF 0.8; ALBI 0.77) and to distinguish between patients with and without cirrhosis (AUC: MELIF 0.83, ALBI 0.79). The MELIF score performed more favourably than the ALBI score and may also be suitable for monitoring mild disease progression. Thus, the MELIF score is promising for closing the gap in the available early-stage liver disease monitoring tools (i.e., identification of liver disease at a potentially reversible stage before chronic liver disease develops).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%