Naoki Takahashi1, Motokazu Sugimoto2, Sarah P Psutka3, Baiyu Chen4, Michael R Moynagh4, Rickey E Carter5. 1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. takahashi.naoki@mayo.edu. 2. Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 3. Department of Urology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 4. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 5. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, USA.
Abstract
BACKGROUND: Computed tomography (CT) has been increasingly used to quantify abdominal muscle and fat in clinical research studies, and multiple studies have shown importance of body composition in predicting clinical outcome. The purpose of study is to compare newly developed semi-automated software (BodyCompSlicer) to commercially available validated software (Slice-O-Matic) for CT body composition analysis. METHODS: CT scans of abdomen at L3 level in 30 patients were analyzed by two reviewers and using two softwares (BodyCompSlicer and Slice-O-Matic). Body composition analysis using BodyCompSlicer was semi-automated. The program automatically segmented subcutaneous fat (SF), skeletal muscle (SM), and visceral fat (VF) areas. Reviewers manually corrected the segmentation using computer-mouse interface as necessary. Body composition analysis using Slice-O-Matic was performed by manually segmenting each area using computer-mouse interface (brush tool). After segmentation, SM, SF, and VF areas were calculated using CT attenuation thresholds. Inter-observer and inter-software variability of measurements were analyzed using intraclass correlation coefficients (ICC) and coefficient of variation (COV). RESULTS: Inter-observer ICC and COV using BodyCompSlicer were 0.997 and 1.5% for SM, 1.000 and 0.8% for SF, and 1.000 and 1.0% for VF, whereas those using Slice-O-Matic were 0.993 and 2.5% for SM, 0.995 and 3.1% for SF, and 0.999 and 2.3% for VF. Inter-software ICCs and COV were 0.995-0.995 and 2.0-2.1% for SM, 0.991-0.994 and 3.4-3.9% for SF, and 0.998-0.998 and 2.8-3.3% for VF. Time to analyze 30 cases was 70-100 min and 150-180 min using BodyCompSlicer and Slice-O-Matic, respectively. CONCLUSION: BodyCompSlicer is comparable to Slice-O-Matic for CT body composition analysis.
BACKGROUND: Computed tomography (CT) has been increasingly used to quantify abdominal muscle and fat in clinical research studies, and multiple studies have shown importance of body composition in predicting clinical outcome. The purpose of study is to compare newly developed semi-automated software (BodyCompSlicer) to commercially available validated software (Slice-O-Matic) for CT body composition analysis. METHODS: CT scans of abdomen at L3 level in 30 patients were analyzed by two reviewers and using two softwares (BodyCompSlicer and Slice-O-Matic). Body composition analysis using BodyCompSlicer was semi-automated. The program automatically segmented subcutaneous fat (SF), skeletal muscle (SM), and visceral fat (VF) areas. Reviewers manually corrected the segmentation using computer-mouse interface as necessary. Body composition analysis using Slice-O-Matic was performed by manually segmenting each area using computer-mouse interface (brush tool). After segmentation, SM, SF, and VF areas were calculated using CT attenuation thresholds. Inter-observer and inter-software variability of measurements were analyzed using intraclass correlation coefficients (ICC) and coefficient of variation (COV). RESULTS: Inter-observer ICC and COV using BodyCompSlicer were 0.997 and 1.5% for SM, 1.000 and 0.8% for SF, and 1.000 and 1.0% for VF, whereas those using Slice-O-Matic were 0.993 and 2.5% for SM, 0.995 and 3.1% for SF, and 0.999 and 2.3% for VF. Inter-software ICCs and COV were 0.995-0.995 and 2.0-2.1% for SM, 0.991-0.994 and 3.4-3.9% for SF, and 0.998-0.998 and 2.8-3.3% for VF. Time to analyze 30 cases was 70-100 min and 150-180 min using BodyCompSlicer and Slice-O-Matic, respectively. CONCLUSION: BodyCompSlicer is comparable to Slice-O-Matic for CT body composition analysis.
Entities:
Keywords:
Body composition; Cachexia; Computed tomography; Sarcopenia
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