A prediction model for skeletal muscle evaluation and computed tomography-defined sarcopenia diagnosis in a predominantly overweight cohort of patients with head and neck cancer.

Content Provider	Europe PMC
Author	Vangelov, Belinda Bauer, Judith Moses, Daniel Smee, Robert
Abstract	PurposeThis study investigates the feasibility of computed tomography (CT)-defined sarcopenia assessment using a prediction model for estimating the cross-sectional area (CSA) of skeletal muscle (SM) in CT scans at the third lumbar vertebra (L3), using measures at the third cervical level (C3) in a predominantly overweight population with head and neck cancer (HNC).MethodsAnalysis was conducted on adult patients with newly diagnosed HNC who had a diagnostic positron emission tomography–CT scan. CSA of SM in CT images was measured at L3 and C3 in each patient, and a predictive formula developed using fivefold cross-validation and linear regression modelling. Correlation and agreement between measured CSA at L3 and predicted values were evaluated using intraclass correlation coefficients (ICC) and Bland–Altman plot. The model’s ability to identify sarcopenia was investigated using Cohen’s Kappa (k).ResultsA total of 109 patient scans were analysed, with 64% of the cohort being overweight or obese. The prediction model demonstrated high level of correlation between measured and predicted CSA measures (ICC 0.954, r = 0.916, p < 0.001), and skeletal muscle index (SMI) (ICC 0.939, r = 0.883, p < 0.001). Bland–Altman plot showed good agreement in SMI, with mean difference (bias) = 0.22% (SD 8.65, 95% CI − 3.35 to 3.79%), limits of agreement (− 16.74 to 17.17%). The model had a sensitivity of 80.0% and specificity of 85.0%, with moderate agreement on sarcopenia diagnosis (k = 0.565, p = 0.004).ConclusionThis model is effective in predicting lumbar SM CSA using measures at C3, and in identifying low SM in a predominately overweight group of patients with HNC.
ISSN	09374477
Journal	European Archives of Oto-Rhino-Laryngology
Volume Number	280
PubMed Central reference number	PMC9813227
Issue Number	1
PubMed reference number	35835910
e-ISSN	14344726
DOI	10.1007/s00405-022-07545-x
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2022-07-14
Publisher Place	Berlin/Heidelberg
Access Restriction	Open
Rights License	Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2022
Subject Keyword	Head and neck cancer Sarcopenia Skeletal muscle Computed tomography Body composition
Content Type	Text
Resource Type	Article
Subject	Otorhinolaryngology