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Updated: Jul 17, 2026
06:48
Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
Published on: June 7, 2024
Future of CT body composition research: Methodological discrepancies and advances
Cecily A Byrne1, Sandra L Gomez2
1Previously with the Cancer Health Education and Career Development Program, University of Illinois Chicago, Chicago, Illinois, USA.
Summary
Computed tomography (CT) body composition analysis faces challenges due to methodological inconsistencies. This review explores newer normalization methods and AI advancements for better clinical integration of body composition data.
Area of Science:
- Medical Imaging
- Radiology
- Body Composition Analysis
Background:
- Computed tomography (CT) is increasingly used for body composition analysis, identifying phenotypes like sarcopenia.
- Clinical integration of CT-based body composition is hindered by methodological discrepancies.
Purpose of the Study:
- To review newer methods for normalizing skeletal muscle measurements in CT body composition studies.
- To summarize and compare CT-derived reference criteria and thresholds for muscle quantity and quality.
- To highlight AI applications and challenges in clinical integration for personalized nutrition.
Main Methods:
- Narrative review of studies published between 2022 and 2025.
- Analysis of newer approaches to normalize skeletal muscle measurements.
- Overview of heterogeneity in CT reference criteria and thresholds.
Main Results:
- Methodological discrepancies persist, impacting clinical integration of CT body composition data.
- Newer normalization approaches for skeletal muscle measurements are emerging.
- Heterogeneity exists in CT reference criteria and thresholds for defining muscle quantity and quality.
Conclusions:
- Standardization of CT body composition methods is crucial for clinical utility.
- Artificial intelligence shows promise for streamlining assessments and enabling personalized nutrition.
- Clear guidance is needed for implementing CT body composition data to improve clinical outcomes.
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