Evidence map›Paper›PMID 40465826›Full record

ArticleClinical transplantation2025

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

William Miller, Kassandra Fate, Jessica Fisher, Jessica Thul, Yousun Ko, Kyung Won Kim, Timothy Pruett, Levi Teigen

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

William MillerDivision of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0002-5984-190X
Kassandra FateDivision of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Jessica FisherDivision of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0001-8857-425X
Jessica ThulUniversity of Minnesota Medical Center, MHealth Fairview, Minneapolis, Minnesota, USA.
Yousun KoDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Kyung Won KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Timothy PruettDivision of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Levi TeigenDepartment of Food Science and Nutrition, University of Minnesota, St Paul, Minnesota, USA.

Funding

Korea Health Industry Development InstituteMinistry of Health and Welfare
6 · The paper itself

Abstract

introductionSarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography (CT) scans are utilized to measure patient core musculature as a measurement of sarcopenia. Methods to extract information on core body musculature can be through either freehand region-of-interest (ROI) or machine learning algorithms to quantitate total body muscle within a given area. This study directly compares these two collection methods leveraging length of stay (LOS) outcomes previously found to be associated with freehand ROI measurements.

methodsA total of 50 individuals were included who underwent liver transplantation from our single center between January 1, 2016, and May 30, 2021, and had a non-contrast abdominal CT scan within 6-months of surgery. CT-derived skeletal muscle measures at the third lumbar vertebrae were obtained using freehand ROI and an automated deep learning system.

resultsCorrelation analysis of freehand psoas muscle measures, psoas area index (PAI) and mean Hounsfield units (mHU), were significantly correlated to the automated deep learning system's total skeletal muscle measures at the level of the L3, skeletal muscle index (SMI) and skeletal muscle density (SMD), respectively (R

conclusionSarcopenia measurements represent an important risk stratification tool for liver transplantation outcomes. For muscle sarcopenia assessment association with LOS, freehand measures of sarcopenia perform similarly to automated deep learning system measurements.

Indexed as

Deep LearningLiver TransplantationPostoperative ComplicationsSarcopeniaTomography, X-Ray ComputedAdultAgedFemaleFollow-Up StudiesHumansLength of StayMaleMiddle AgedPrognosisRetrospective StudiesRisk Factorsfrailtyliver transplantsarcopenia

Identifiers

PMID40465826
PMCPMC12136726

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