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Background
Cachexia is common, costly, debilitating, and deadly. Cachexia is defined as the involuntary loss of muscle mass, fat mass, or both that occurs with chronic diseases. For decades, the “appropriateness” of body weight has been defined by BMI. However, there is now a realization that BMI does not assess body composition, including the quality and distribution of muscle and fat mass, nor does it account for underlying biomarkers of disease-related fluid retention that affect body weight, such as ascites and anasarca. The purpose of this educational exhibit is to explore advances in our understanding of cachexia and describe innovative imaging practices, including the use of artificial intelligence, that have the potential to augment our ability to screen for cachexia, diagnose it, and evaluate the effectiveness of interventions in a longitudinal fashion.

Educational Goals / Teaching Points
Many chronic diseases are associated with cachexia, including cancer, congestive heart failure, chronic kidney disease, and chronic obstructive pulmonary disease. Because cachexia is often under recognized in clinical practice, it is associated with increased mortality, hospital length of stay, costs, and readmissions. Timely diagnosis can ameliorate these adverse effects even as treatment varies with the severity of cachexia as well as its underlying etiology. Imaging, with the assistance of automated body composition algorithms, can augment the efficiency of imaging evaluation and lead to more a timely diagnosis and treatment of cachexia.

Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
Cachexia can be evaluated with multiple imaging modalities including dual-energy x-ray absorptiometry, CT, PET, and MRI. By CT, the presence of low muscle mass can be determined from sex-specific reference standards. The composition of muscle and fat density can also be used to identify patients at high risk for poor outcomes. Opportunistic CT allows abnormalities in muscle and fat quality and distribution to be quantified in scans performed for other indications. A patient’s tissue lipid concentration and distribution can also be quantified from MRI, enabling providers to differentiate between cachexia and metabolic syndrome. Additionally, diffuse T1 hypointensity and hyperintensity seen on fat-suppressed, fluid-sensitive sequences help radiologists diagnose serous atrophy of the bone marrow, a key imaging biomarker of cachexia.

Conclusion
In conclusion, radiologists can use automated body composition measurements to diagnose cachexia accurately and efficiently by reporting objective tissue metrics, including temporal changes in the quality and distribution of fat and muscle mass. The application of these tools has the potential to reduce the morbidity and mortality associated with cachexia.