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Abstract
The oncology community has shown growing interest to understand how body composition measures can be utilized to improve cancer treatment and survivorship care for about 20 million individuals diagnosed with cancer annually. Recent observational studies demonstrate that muscle and adipose tissue distribution are risk factors for clinical outcomes such as postoperative complications, and worse overall survival. There is an emergent recognition that body mass index (BMI) is neither adequate to identify patients with adverse health outcomes due to poor muscle health or excess adiposity, nor does BMI accurately classify the distribution of adiposity. Abdominal CT is a most frequently imaging examination for a wide variety of clinical indications, but it is only used to diagnose the immediate problem. Additionally, each CT examination contains very robust data on body composition which generally goes unused in routine clinical practice. The field is eager to identify therapeutic interventions that modify body composition and reduce the incidence of poor clinical outcomes in this population. Large scale population based screening is feasible now by making all of these relevant biometric measures fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment.
Acknowledgement
We acknowledge the help extended by Dr. Venkatraman Pitchaikannu, Research Fellow in the Department of Radiotherapy & Radiation Medicine, IMS, BHU, Varanasi in compilation of this manuscript.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
Data Availability Statement
All the data used in this manuscript have been taken from already published articles in peer reviewed journals which are generally permitted and no specific permission is required.