Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.

Autor: Ackermans LLGC; Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands; Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands. Electronic address: l.ackermans@maastrichtuniversity.nl., Volmer L; Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands., Timmermans QMMA; Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands., Brecheisen R; Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands., Damink SMWO; Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands; Department of General, Visceral and Transplantation Surgery, RWTH University Hospital Aachen Aachen 52074, Germany., Dekker A; Clinical Data Science, Faculty of Health Medicine and Lifesciences, Maastricht University, Paul Henri Spaaklaan 1, Maastricht 6229 GT, the Netherlands., Loeffen D; Department of Radiology, Maastricht University Medical Centre+, 6229 HX Maastricht, the Netherlands., Poeze M; Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands., Blokhuis TJ; Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands., Wee L; Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands; Clinical Data Science, Faculty of Health Medicine and Lifesciences, Maastricht University, Paul Henri Spaaklaan 1, Maastricht 6229 GT, the Netherlands., Ten Bosch JA; Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands.
Jazyk: angličtina
Zdroj: Injury [Injury] 2022 Nov; Vol. 53 Suppl 3, pp. S30-S41. Date of Electronic Publication: 2022 Jun 02.
DOI: 10.1016/j.injury.2022.05.004
Abstrakt: Introduction: Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice.
Materials and Methods: A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale.
Results: Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating.
Conclusion: A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.
Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses r interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
(Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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