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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
The relationship between imaging-based body composition parameters and disease prognosis in patients with endometrial
Elçin Aydın1,2, Ezgi Güvel Verdi1, Mücahit Furkan Balci3
1Department of Radiology, Izmir Tepecik Training and Research Hospital, University of Health Sciences, Izmir, Turkey.
Background:
Obesity is known as a risk factor for endometrial cancer (EC). Only a few studies investigate the relationship between sarcopenia and sarcopenic obesity and EC. In this study, our aim was to investigate the relationship between the cross-sectional imaging-based body composition parameters and the disease prognosis in low-grade (LG) and high-grade (HG) EC.
Materials And Methods:
We conducted a retrospective study in women diagnosed with low and high-grade EC between January 2014 and May 2022 who had abdominal MRI and thorax CT as a part of routine staging workup. We used the skeletal muscle index (SMI) at the level of the third lumbar vertebra to assess sarcopenia on CT. The T2-weighted sequence at the level of the L2-L3 intervertebral disc is used for visceral fat area (VFA), subcutaneous fat area (SFA), and total fat area (TFA). Two radiologists in consensus, calculated the parameters.
Results:
A total of 250 EC patients (144 low-grade EC, 106 high-grade EC).Sarcopenia was observed in 122 (48.8%) patients, and sarcopenic obesity was found in 82 (32.8%) patients. Although there was an increase in VFA in cases with high-grade EC, there was no significant difference in terms of SFA. Additionally, the frequency of sarcopenia and sarcopenic obesity was higher in cases with high-grade EC. There was no association between sarcopenia and age, histological type, FIGO staging, or comorbidity in the univariate analysis. However, BMI was found to be associated with sarcopenia.
Conclusions:
Quantitative radiological measurement of sarcopenia, sarcopenic obesity, and body fat composition can be used as novel parameters in the prediction of disease prognosis in endometrial cancer.
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