Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients. (November 2022)

• Record Type: Journal Article
• Authors: Ackermans, Leanne L.G.C.; Volmer, Leroy; Timmermans, Quince M.M.A.; Brecheisen, Ralph; Damink, Steven M.W. Olde; Dekker, Andre; Loeffen, Daan; Poeze, Martijn; Blokhuis, Taco J.; Wee, Leonard; Ten Bosch, Jan A.
• Is Part Of: Injury. Volume 53(2022)Supplement 3
• Page Start: S30
• Page End: S41
• Publication Date: 2022-11
• Subjects: Sarcopenia -- Computed tomography -- Deep learning neural networks -- Automated segmentation -- Truncal muscle -- Subcutaneous adipose -- Visceral adipose -- Clinical application of deep learning; BMI Body Mass Index -- CCC Concordance Correlation Coefficient -- CSA Cross Sectional Area -- CT Computer Tomography -- DLNN Deep Learning Neural Network -- DSC Dice Similarity Coefficient -- EWGSOP European Working Group on Sarcopenia -- HU Hounsfield unit -- ICC Intra-class Correlation -- IMAT Intramuscular Adipose Tissue -- ISS Injury Severity Score -- LOA Limits of Agreement interval -- SAT Subcutaneous Adipose Tissue -- SATI Subcutaneous Adipose Tissue Index -- SMI Skeletal Muscle Index -- SMRA Skeletal Muscle Radiation Attenuation -- VAT Visceral Adipose Tissue -- VATI Visceral Adipose Tissue Index; Wounds and injuries -- Surgery -- Periodicals; Accidents -- Periodicals; Wounds and Injuries -- surgery -- Periodicals; Lésions et blessures -- Chirurgie -- Périodiques; Electronic journals; Electronic journals; 617.1
• DOI: 10.1016/j.injury.2022.05.004
• Languages: English
• ISSNs: 0020-1383

Abstract

Abstract: 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.