Use of automated assessment for determining associations of low muscle mass and muscle loss with overall survival in patients with colorectal cancer - A validation study.

Karel C Smit¹, Jeroen W G Derksen², Sophie A Kurk¹, Pim Moeskops³, Miriam Koopman⁴, Wouter B Veldhuis⁵, Anne M May⁶

¹Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands.

²Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands.

³Quantib, Westblaak 130, 3012 KM Rotterdam, the Netherlands.

⁴Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands.

⁵Department of Radiology, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands.

⁶Department of Epidemiology and Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, PO Box 85500, 3508 GA, Utrecht, the Netherlands. Electronic address: a.m.may@umcutrecht.nl.

PubMed DOI

Abstract

BACKGROUND: Low muscle mass and skeletal muscle mass (SMM) loss are associated with adverse patient outcomes, but the time-consuming nature of manual SMM quantification prohibits implementation of this metric in clinical practice. Therefore, we assessed the feasibility of automated SMM quantification compared to manual quantification. We evaluated both diagnostic accuracy for low muscle mass and a…

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