William Miller, Kassandra Fate, Jessica Fisher, Jessica Thul, Yousun Ko, Kyung Won Kim, Timothy Pruett, Levi Teigen
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This study directly compares these two collection methods leveraging length of stay (LOS) outcomes previously found to be associated with freehand ROI measurements.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Correlation 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<sup>2</sup> = 0.4221; <i>p</i> value < 0.0001; R<sup>2</sup> = 0.6297; <i>p</i> value < 0.0001). The automated deep learning model's SMI predicted ∼20% of the variability (R<sup>2</sup> = 0.2013; hospital length of stay) while the PAI variable only predicted about 10% of the variability (R<sup>2</sup> = 0.0919; total healthcare length of stay) of the length of stay variables. In contrast, both the freehand ROI mHU and the automated deep learning model's muscle density variables were associated with ∼20% of the variability in the inpatient length of stay (R<sup>2</sup> = 0.2383 and 0.1810, respectively) and total healthcare length of stay variables (R<sup>2</sup> = 0.2190 and 0.1947, respectively).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Sarcopenia 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.</p>\n </section>\n </div>","PeriodicalId":10467,"journal":{"name":"Clinical Transplantation","volume":"39 6","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ctr.70201","citationCount":"0","resultStr":"{\"title\":\"Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System\",\"authors\":\"William Miller, Kassandra Fate, Jessica Fisher, Jessica Thul, Yousun Ko, Kyung Won Kim, Timothy Pruett, Levi Teigen\",\"doi\":\"10.1111/ctr.70201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>Sarcopenia, 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A 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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Correlation 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<sup>2</sup> = 0.4221; <i>p</i> value < 0.0001; R<sup>2</sup> = 0.6297; <i>p</i> value < 0.0001). The automated deep learning model's SMI predicted ∼20% of the variability (R<sup>2</sup> = 0.2013; hospital length of stay) while the PAI variable only predicted about 10% of the variability (R<sup>2</sup> = 0.0919; total healthcare length of stay) of the length of stay variables. In contrast, both the freehand ROI mHU and the automated deep learning model's muscle density variables were associated with ∼20% of the variability in the inpatient length of stay (R<sup>2</sup> = 0.2383 and 0.1810, respectively) and total healthcare length of stay variables (R<sup>2</sup> = 0.2190 and 0.1947, respectively).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Sarcopenia measurements represent an important risk stratification tool for liver transplantation outcomes. 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Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System
Introduction
Sarcopenia, 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.
Methods
A 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.
Results
Correlation 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 (R2 = 0.4221; p value < 0.0001; R2 = 0.6297; p value < 0.0001). The automated deep learning model's SMI predicted ∼20% of the variability (R2 = 0.2013; hospital length of stay) while the PAI variable only predicted about 10% of the variability (R2 = 0.0919; total healthcare length of stay) of the length of stay variables. In contrast, both the freehand ROI mHU and the automated deep learning model's muscle density variables were associated with ∼20% of the variability in the inpatient length of stay (R2 = 0.2383 and 0.1810, respectively) and total healthcare length of stay variables (R2 = 0.2190 and 0.1947, respectively).
Conclusion
Sarcopenia 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.
期刊介绍：
Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored.
Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include:
Immunology and immunosuppression;
Patient preparation;
Social, ethical, and psychological issues;
Complications, short- and long-term results;
Artificial organs;
Donation and preservation of organ and tissue;
Translational studies;
Advances in tissue typing;
Updates on transplant pathology;.
Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries.
Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.
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