Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions
Abstract
:1. Introduction
2. Imaging-Based Body Composition Analysis
2.1. Muscle Mass
2.2. Skeletal Muscle Quality
2.3. Visceral Fat Content
2.4. Bone Density
2.5. Arterial Calcifications
2.6. Other CT-Based Quantitative Metrics
3. Clinical Applications of CT Body Composition
3.1. Cancer
3.2. Liver Disease
3.3. Inflammatory Bowel Disease (IBD)
3.4. Kidney Disease
3.5. COVID-19
3.6. Cardiovascular Diseases
3.7. Critical Illness
3.8. Contrast Dose Adjustment
4. CT Body Composition Analysis—Technical Considerations
5. Future Directions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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CT Body Composition Metrics | Analysis Method | Terminology of an Abnormal Value | Clinical Applications |
---|---|---|---|
Skeletal Muscle Index (SMI) (in cm2/m2) | Localization and Segmentation of Skeletal muscle at the appropriate location (commonly L3) followed by calculation of the total skeletal muscle cross-sectional area divided by height squared, resulting in SMI calculation | Sarcopenia | Predict postoperative outcomes and the risk of various disease outcomes including cancer, cirrhosis, Inflammatory bowel disease, kidney disease, Severe COVID-19 and critical illness [16,17,18,19,20,21,22,23,24,25,26,27]. |
Skeletal Muscle Density (in HU) | After muscle segmentation, calculation of the mean muscle radiation attenuation of a muscle tissue excluding inter- and intra- muscular adipose tissue. This gives a muscle density expressed in Hounsfield units (HU). A higher attenuation indicates a low muscle density. | Myosteatosis or low muscle quality or muscle fat infiltration | Associated with poor metabolic function and worse perioperative morbidity and mortality. Can predict the risk of long-term oncological outcomes specially in those receiving treatments. It’s also an independent predictor of mortality in necrotizing pancreatitis, COVID-19 and those undergoing hemodialysis [28,29,30,31,32,33,34]. |
Adipose Tissue
| CT slice from an appropriate location is segmented and a region of interest(ROI) pass through the abdomen separating the abdominal wall from fat in a smooth manner due to the high difference in density and intensity, thus separating SAT from VAT. Automated analysis of a ROI that includes all similar grey pixels of VAT then results in a sizable area. |
| Predictor of major cardiovascular events, nonalcoholic fatty liver cirrhosis, kidney disease, cancer, metabolic syndrome, severe COVID-19 and mortality in asymptomatic screening population [28,29,35,36,37,38] |
Bone Mineral Density (BMD) (in HU) | The mean vertebral BMD is measured by placing a ROI commonly in L1-L3 vertebral bodies at the coronal, sagittal and axial images. Automated analysis of the cortical and trabecular area/BMD is obtained in HU. |
| Can accurately screen for osteoporosis and predict future risk of osteoporotic fractures. Can also aid with measurement of syndesmophytes and predict progression of ankylosing spondylitis [39,40,41,42,43] |
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Elhakim, T.; Trinh, K.; Mansur, A.; Bridge, C.; Daye, D. Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics 2023, 13, 968. https://doi.org/10.3390/diagnostics13050968
Elhakim T, Trinh K, Mansur A, Bridge C, Daye D. Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics. 2023; 13(5):968. https://doi.org/10.3390/diagnostics13050968
Chicago/Turabian StyleElhakim, Tarig, Kelly Trinh, Arian Mansur, Christopher Bridge, and Dania Daye. 2023. "Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions" Diagnostics 13, no. 5: 968. https://doi.org/10.3390/diagnostics13050968