title: Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration.
abstract: Muscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration.
authors.affiliation: Institute for Biomedical and Neural Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland.
authors.forename: K J
authors.identifier: 0000-0002-6591-4116
authors.initials: KJ
authors.lastname: Edmunds
authors.affiliation: Institute for Biomedical and Neural Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland.
authors.forename: Í
authors.initials: Í
authors.lastname: Árnadóttir
authors.affiliation: Institute for Biomedical and Neural Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland.
authors.forename: M K
authors.initials: MK
authors.lastname: Gíslason
authors.affiliation: IRCCS Fondazione Ospedale San Camillo, Via Alberoni 70, Lido, 30126 Venezia, Italy.
authors.forename: U
authors.initials: U
authors.lastname: Carraro
authors.affiliation: Institute for Biomedical and Neural Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland; Department of Rehabilitation, Landspítali, Hringbraut, 101 Reykjavík, Iceland.
authors.forename: P
authors.identifier: 0000-0002-5049-4817
authors.initials: P
authors.lastname: Gargiulo
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D015331:Cohort Studies; D003720:Densitometry / Q000379:methods*; D064368:Healthy Volunteers; D006801:Humans; D007866:Leg / Q000000981:diagnostic imaging / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging / Q000503:physiopathology; D009133:Muscular Atrophy / Q000000981:diagnostic imaging / Q000503:physiopathology*; D011857:Radiographic Image Interpretation, Computer-Assisted; D012044:Regression Analysis; D055948:Sarcopenia / Q000000981:diagnostic imaging / Q000503:physiopathology*; D012584:Sciatic Nerve / Q000293:injuries; D013119:Spinal Cord Injuries / Q000000981:diagnostic imaging / Q000503:physiopathology; D014057:Tomography, X-Ray Computed