Abstract

The analysis of body composition from computed tomography (CT) imaging has become widespread. However, the methodology used is far from established. Two main software packages are commonly used for body composition analysis, with results used interchangeably. However, the equivalence of these has not been well established. The aim of this study was to compare the results of body composition analysis performed using the two software packages to assess their equivalence. Triphasic abdominal CT scans from 50 patients were analyzed for a range of body composition measures at the third lumbar vertebral level using OsiriX (v7.5.1, Pixmeo, Switzerland) and SliceOmatic (v5.0, TomoVision, Montreal, Canada) software packages. Measures analyzed were skeletal muscle index (SMI), fat mass (FM), fat-free mass (FFM), and mean skeletal muscle Hounsfield Units (SMHU). The overall mean SMI calculated using the two software packages was significantly different (SliceOmatic 51.33 versus OsiriX 53.77, P < 0.0001), and this difference remained significant for non-contrast and arterial scans. When FM and FFM were considered, again the results were significantly different (SliceOmatic 33.7 versus OsiriX 33.1 kg, P < 0.0001; SliceOmatic 52.1 versus OsiriX 54.2 kg, P < 0.0001, respectively), and this difference remained for all phases of CT. Finally, when analyzed, mean SMHU was also significantly different (SliceOmatic 32.7 versus OsiriX 33.1 HU, P = 0.046). All four body composition measures were statistically significantly different by the software package used for analysis; however, the clinical significance of these differences is doubtful. Nevertheless, the same software package should be used if serial measurements are being performed.

Highlights

  • Computed tomography (CT) analysis of body composition to measure fat mass (FM) and fat free mass (FFM), calculate skeletal muscle index (SMI), and diagnose sarcopenia and myosteatosis has become increasingly common, with literature linking sarcopenia and myosteatosis with reduced overall survival [1, 2], decreased tolerance to chemotherapy [3, 4] and increased complications [5, 6] following surgery in patients presenting with various types of malignancy

  • Slices were analysed as Digital Imaging and Communication in Medicine (DICOM) images obtained from the Picture Archiving and Communication System (PACS)

  • Of the 50 patients included during the study period of April 2014 to September 2015 there were 33 males and 17 females, with a mean body mass index (BMI) of 30.4 (SD 4.0) kg/m2

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Summary

Introduction

Computed tomography (CT) analysis of body composition to measure fat mass (FM) and fat free mass (FFM), calculate skeletal muscle index (SMI), and diagnose sarcopenia and myosteatosis has become increasingly common, with literature linking sarcopenia and myosteatosis with reduced overall survival [1, 2], decreased tolerance to chemotherapy [3, 4] and increased complications [5, 6] following surgery in patients presenting with various types of malignancy.the methodology for calculating body composition from CT images is variable between studies, from the nature of the CT scan used including the vertebral level, to the use of contrast medium, to the software used to perform the analysis. The impact of the use of contrast medium in CT scanning in body composition analysis has previously been recognised to have a significant effect upon results, especially the diagnosis of myosteatosis [7, 8]. Despite these inconsistencies in analysis, the results of these studies are used interchangeably, with the definition of neither sarcopenia or myosteatosis stipulating any conditions about how these derived values are calculated. There are currently two software packages used commonly to analyse body composition from CT scans: SliceOmatic (TomoVision, Montreal, Canada) and OsiriX (Pixmeo, Switzerland), the results of which are used interchangeably. One study in patients with rectal cancer [9] has suggested that SliceOmatic, ImageJ (National Institutes of Health, Bethesda, MD, USA), FatSeg [Biomedical Imaging Group Rotterdam of Erasmus MC, Rotterdam, The Netherlands, using MeVisLab (Mevis Medical Solutions, Bremen, Germany)]

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