Advances in body composition imaging for diagnosis and monitoring: From radiologic assessment to precision medicine.
Authors
Affiliations (4)
Affiliations (4)
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
- Section of Holistic and Multidisciplinary Medicine, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
- Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan, ROC.
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Abstract
Body composition (BC) is a key determinant of metabolic risk, treatment tolerance, and long-term outcomes. The distribution of skeletal muscle, visceral adipose tissue (VAT), and ectopic fat provides greater prognostic information than total body weight alone does. However, conventional anthropometric measures, such as body mass index, cannot distinguish skeletal muscle from VAT and ectopic fat, potentially obscuring clinically relevant phenotypes such as sarcopenia, myosteatosis, and sarcopenic obesity. This review addresses the "BMI paradox" by evaluating how different imaging modalities identify high-risk phenotypes, such as sarcopenic obesity, that are not detected by standard anthropometric measures. Radiological imaging enables high-precision quantification by using validated metrics. In Asian populations, clinical interpretation requires population-specific thresholds. For example, the Asian Working Group for Sarcopenia 2019 criteria define low muscle mass as an appendicular skeletal muscle index of <7.0 kg/m² for men and <5.4 kg/m² for women. Computed tomography is the reference standard for BC assessment, and Hounsfield units are used to identify myosteatosis, typically within a range of -29 to +150 HU for skeletal muscle. Magnetic resonance imaging enables scanner-independent quantification of ectopic fat by using proton density fat fraction and is used for longitudinal monitoring of metabolic interventions. Studies report a substantial reduction in VAT within 12 months after metabolic surgery. Artificial intelligence has changed the workflow of BC analysis by reducing manual segmentation time from 15-30 minutes per case to seconds while maintaining high concordance with manual methods. This improvement enables opportunistic screening using existing diagnostic scans without additional radiation exposure or cost. Integration of radiologic assessment with quantitative biomarkers supports the use of BC imaging as a structured "metabolic report," which can guide therapeutic decision-making and support proactive health risk management.