Einzeltreffer — DigiBib

Background: Measurement of cross-sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmentation is tedious and is thus restricted to research. Automated solutions are required for use in clinical practice.

Purpose: The aim of this study was to compare the reliability of two automated solutions for the measurement of CSMA.

Methods: We conducted a retrospective analysis of CT images in our hospital database. We included consecutive individuals hospitalized at the Grenoble University Hospital in France between January and May 2018 with abdominal CT images and sagittal reconstruction. We used two types of software to automatically segment skeletal muscle: ABACS, a module of the SliceOmatic software solution "ABACS-SliceOmatic," and a deep learning-based solution called "AutoMATiCA." Manual segmentation was performed by a medical expert to generate reference data using "SliceOmatic." The Dice similarity coefficient (DSC) was used to measure overlap between the results of the manual and the automated segmentations. The DSC value for each method was compared with the Mann-Whitney U test.

Results: A total of 676 hospitalized individuals was retrospectively included (365 males [53.8%] and 312 females [46.2%]). The median DSC for SliceOmatic vs AutoMATiCA (0.969 [5th percentile: 0.909]) was greater than the median DSC for SliceOmatic vs. ABACS-SliceOmatic (0.949 [5th percentile: 0.836]) (p < 0.001).

Conclusions: AutoMATiCA, which used artificial intelligence, was more reliable than ABACS-SliceOmatic for skeletal muscle segmentation at the L3 level in a cohort of hospitalized individuals. The next step is to develop and validate a neural network that can identify L3 slices, which is currently a fastidious process.

Titel:	A comparative study of two automated solutions for cross-sectional skeletal muscle measurement from abdominal computed tomography images.
Autor/in / Beteiligte Person:	Charrière, K ; Boulouard, Q ; Artemova, S ; Vilotitch, A ; Ferretti, GR ; Bosson, JL ; Moreau-Gaudry, A ; Giai, J ; Fontaine, E ; Bétry, C
Link:	Full Text Finder (Volltext)
Zeitschrift:	Medical physics, Jg. 50 (2023-08-01), Heft 8, S. 4973-4980
Veröffentlichung:	2017- : Hoboken, NJ : John Wiley and Sons, Inc. ; <i>Original Publication</i>: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics., 2023
Medientyp:	academicJournal
ISSN:	2473-4209 (electronic)
DOI:	10.1002/mp.16261
Schlagwort:	Male Female Humans Retrospective Studies Reproducibility of Results Cross-Sectional Studies Muscle, Skeletal diagnostic imaging Artificial Intelligence Tomography, X-Ray Computed methods
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Med Phys] 2023 Aug; Vol. 50 (8), pp. 4973-4980. <i>Date of Electronic Publication: </i>2023 Feb 16. MeSH Terms: Artificial Intelligence* ; Tomography, X-Ray Computed* / methods ; Male ; Female ; Humans ; Retrospective Studies ; Reproducibility of Results ; Cross-Sectional Studies ; Muscle, Skeletal / diagnostic imaging References: Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31. doi:10.1093/ageing/afy169. ; Sousa AS, Guerra RS, Fonseca I, Pichel F, Amaral TF. Sarcopenia and length of hospital stay. Eur J Clin Nutr. 2016;70(5):595. doi:10.1038/ejcn.2015.207. ; Xu J, Wan CS, Ktoris K, Reijnierse EM, Maier AB. Sarcopenia is associated with mortality in adults: a systematic review and meta-analysis. Gerontology. 2022;68(4):1-16. doi:10.1159/000517099. ; Sivaharan A, Boylan L, Witham MD, Nandhra S. Sarcopenia in patients undergoing lower limb bypass surgery is associated with higher mortality and major amputation rates. Ann Vasc Surg. 2021;75:227-236. doi:10.1016/j.avsg.2021.02.022. ; Prado CM, Lieffers JR, McCargar LJ, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 2008;9(7):629-635. doi:10.1016/S1470-2045(08)70153-0. ; Findlay M, White K, Stapleton N, Bauer J. Is sarcopenia a predictor of prognosis for patients undergoing radiotherapy for head and neck cancer? A meta-analysis. Clin Nutr. 2021;40(4):1711-1718. doi:10.1016/j.clnu.2020.09.017. ; Cox MC, Booth M, Ghita G, et al. The impact of sarcopenia and acute muscle mass loss on long-term outcomes in critically ill patients with intra-abdominal sepsis. J Cachexia Sarcopenia Muscle. 2021;12(5):1203-1213. doi:10.1002/jcsm.12752. ; Albano D, Messina C, Vitale J, Sconfienza LM. Imaging of sarcopenia: old evidence and new insights. Eur Radiol. 2020;30(4):2199-2208. doi:10.1007/s00330-019-06573-2. ; Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci. 2019;74(10):1671-1678. doi:10.1093/gerona/glz034. ; Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016;35(2):512-520. doi:10.1109/TMI.2015.2479252. ; Chung H, Cobzas D, Birdsell L, Lieffers J, Baracos V. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. Med Imaging. Visualization, Image-Guided Procedures, and Modeling. 2009;7261:72610K. doi:10.1117/12.812412. ; Blanc-Durand P, Schiratti JB, Schutte K, et al. Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment. Diagn Interv Imaging. 2020;101(12):789-794. doi:10.1016/j.diii.2020.04.011. ; Borrelli P, Kaboteh R, Enqvist O, et al. Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp. 2021;5:11. doi:10.1186/s41747-021-00210-8. ; Paris MT, Tandon P, Heyland DK, et al. Automated body composition analysis of clinically acquired computed tomography scans using neural networks. Clin Nutr. 2020;39(10):3049-3055. doi:10.1016/j.clnu.2020.01.008. ; Caudron L, Bussy A, Artemova S, et al. Sarcopenia diagnosis: comparison of automated with manual computed tomography segmentation in clinical routine. JCSM Rapid Commun. 2021;4(2):103-110. doi:10.1002/rco2.37. ; Cespedes Feliciano EM, Popuri K, Cobzas D, et al. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. J Cachexia Sarcopenia Muscle. 2020;11(5):1258-1269. doi:10.1002/jcsm.12573. Published online April 20. ; Paris MT, Furberg HF, Petruzella S, Akin O, Hötker AM, Mourtzakis M. Influence of contrast administration on computed tomography-based analysis of visceral adipose and skeletal muscle tissue in clear cell renal cell carcinoma. JPEN J Parenter Enteral Nutr. 2018;42(7). doi:10.1002/jpen.1067. Published online January 19. ; van Vugt JLA, Coebergh van den Braak RRJ, Schippers HJW, et al. Contrast-enhancement influences skeletal muscle density, but not skeletal muscle mass, measurements on computed tomography. Clin Nutr. 2018;37(5):1707-1714. doi:10.1016/j.clnu.2017.07.007. Published online July 14. ; Artemova S, Madiot PE, Caporossi A, Group P, Mossuz P, Moreau-Gaudry A. PREDIMED: clinical data warehouse of Grenoble Alpes University Hospital. Stud Health Technol Inform. 2022;290:1068-1069. doi:10.3233/SHTI190464. ; Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Published online May 18, 2015. Accessed July 21, 2021. https://arxiv.org/abs/1505.04597v1. ; Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297-302. doi:10.2307/1932409. ; Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. doi:10.1016/j.jcm.2016.02.012. ; Mahajan V, Venugopal VK, Murugavel M, Mahajan H. The algorithmic audit: working with vendors to validate radiology-AI algorithms-how we do it. Acad Radiol. 2020;27(1):132-135. doi:10.1016/j.acra.2019.09.009. ; Kim DW, Ha J, Ko Y, et al. Reliability of skeletal muscle area measurement on CT with different parameters: a phantom study. Korean J Radiol. 2021;22(4):624-633. doi:10.3348/kjr.2020.0914. ; Belharbi S, Chatelain C, Hérault R, et al. Spotting L3 slice in CT scans using deep convolutional network and transfer learning. Comput Biol Med. 2017;87:95-103. doi:10.1016/j.compbiomed.2017.05.018. ; Chen Y, Gao Y, Li K, Zhao L, Zhao J. Vertebrae identification and localization utilizing fully convolutional networks and a hidden markov model. IEEE Trans Med Imaging. 2020;39(2):387-399. doi:10.1109/TMI.2019.2927289. ; Lenchik L, Barnard R, Boutin RD, et al. Automated muscle measurement on chest CT predicts all-cause mortality in older adults from the national lung screening trial. J Gerontol A Biol Sci Med Sci. 2020;76(2):277-285. doi:10.1093/gerona/glaa141. Grant Information: Délégation Régionale à la Recherche Clinique du Centre Hospitalier Universitaire Grenoble Alpes Contributed Indexing: Keywords: artificial intelligence; body composition; computational neural networks; sarcopenia; skeletal muscle; software validation Entry Date(s): Date Created: 20230201 Date Completed: 20230815 Latest Revision: 20230815 Update Code: 20240513

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

A comparative study of two automated solutions for cross-sectional skeletal muscle measurement from abdominal computed tomography images.