ArticleEuropean journal of radiology2022
Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer.
Article in European journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
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- Age- and BMI-Dependent Psoas and Gluteus Muscle Mass in 27,805 Participants of the Population-Based German National Cohort (NAKO Gesundheitsstudie): A Deep-Learning 3T MRI Study.Diagnostics (Basel, Switzerland) · 2026Article
- Reliable Radiologic Skeletal Muscle Area Assessment - A Biomarker for Cancer Cachexia Diagnosis.medRxiv : the preprint server for health sciences · 2025Article
- Risk factors for parastomal hernia following abdominoperineal resection.Frontiers in oncology · 2025Article
- Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population.EBioMedicine · 2024Article
- Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.Journal of cachexia, sarcopenia and muscle · 2024Article
- Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.JCO clinical cancer informatics · 2024Article
- Combined Prognostic Impact of Low Muscle Mass and Hypoalbuminemia in Patients Hospitalized for Heart Failure: A Retrospective Cohort Study.Journal of the American Heart Association · 2024Article
- Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.Frontiers in nuclear medicine · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeManual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer.
methodWe performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height
resultsThere was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods.
conclusionsAutomated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.
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