Article Text
Abstract
Introduction/Background It is known that obesity is a risk factor for endometrial cancer. Body composition can be determined from the standard of care imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI). We aim to investigate the relationship between the imaging-based body composition parameters and clinicopathologic features in patients with endometrial cancer.
Methodology We conducted a retrospective study in women diagnosed with high-grade (HG; non-endometrioid and FIGO G3 endometrioid) and low grade (LG; G1–2 endometrioid) endometrial cancer (EC) between Jan 2014-May 2022, who had abdominopelvic MRI and thorax CT scans as parts of preoperative routine staging work-up. Sarcopenia (S; SMI≤41 cm2/m2) and obesity (BMI ≥30 kg/m2) combination is called sarcopenic obesity (SO). Skeletal muscle index (SMI) at L3 level was used to assess sarcopenia on CT. After segmentation and quantification of adipose tissue on T2-weighted axial MR image at L2 level, visceral (VFA), subcutaneous (SFA) and total fat area (TFA) were calculated using MRI volume-analyzing software (AWI Server 3.2 Ext 1.0;GE Healthcare). Two radiologists calculated the imaging parameters in consensus. The relationship between sarcopenia and clinicopathological features was evaluated using univariate analysis. P values less than 0.05 were considered statistically significant.
Results A total of 250 EC patients (144 LG, 106 HG; mean age 71 years (range, 48–92 years); mean BMI 29.71±6.07) were analyzed. VFA, SFA, and TFA were measured as 119.22±51.07 cm2, 119.38±44.29 cm2, and 238.64±84.38 cm2, respectively. Sarcopenia and SO was observed in 122 (48.8%), and 82 (32.8%) patients, respectively. VFA and the frequency of sarcopenia and SO was higher in patients with HG than LG EC. There was no association between sarcopenia and age, histological type, FIGO staging, or comorbidity in the univariate analysis except BMI (p<0.001).
Conclusion Sarcopenia, sarcopenic obesity, and VFA can be used as novel parameters in prediction of high-grade endometrial cancer.
Disclosures No disclosures