Patients with endometrial cancer who have low volumetric skeletal muscle measurements had lower 3-year overall and progression-free survival rates.
A novel biomarker, waist skeletal muscle volume, could be predictive of patient prognosis in endometrial cancer, according to findings from a retrospective cohort study published in Insights Imaging; the report also suggests a role for artificial intelligence (AI) in body composition assessment of patients.
Patients were separated into sarcopenia (< 39.0 cm2/m2; n = 177) and non-sarcopenia cohorts (≥ 39.0 cm2/m2; n = 208), and no significant differences in progression-free survival (PFS) and overall survival (OS) were identified. However, when patients were stratified by volumetric skeletal muscle index (SMI; with <206.0 cm3/m3 as the cutoff), notable variance was identified for both PFS and OS. In the low-volumetric SMI group (n = 192) the 3-year PFS rate was 77.3% and the 3-year OS rate was 92.8% compared with 88.8% (P = 0.004) and 99.4% (P = 0.003), respectively, in the high-volumetric SMI group (n = 193).
“Compared to the [third lumbar vertebra], SMI, the low volumetric SMI might better reflect the presence of sarcopenia,” Lee and colleagues said.
More than 400,000 cases of endometrial cancer are diagnosed globally each year, with cases steadily rising in South Korea, which is believed to be due in part to a significant increase in obesity. Obesity, the investigators stated, has been linked with an increased risk of endometrial cancer, and research has suggested that body mass index (BMI) can be predictive of an increased risk of death in patients with endometrial cancer. However, investigators stated that sarcopenia has become a topic of interest in recent years, as researchers have linked the condition with adverse survival outcomes in breast, lung, and gastric cancers, among others. In the case of endometrial cancer, early data have been mixed. In the study, investigators sought to leverage the latest technology in volumetric body composition measurement to determine whether the greater precision afforded by AI might yield more clarity into the question of sarcopenia’s impact on survival in endometrial cancer.
Notably, previous studies calculated skeletal muscle area based on a single CT image, with previous findings indicating that L3 is indicative of total body muscle mass and adipose tissue. However, more precise technology could be a more valuable option.
“Beyond the areal measurement, recent technological advances enable the volumetric measurement of a single body composition component, such as skeletal muscle, visceral fat, and subcutaneous fat, from the CT scans that were not feasible due to the requirement of substantial time and human effort,” the authors wrote.
This new measurement paired with AI can process vast amounts of imaging data to quickly calculate key metrics.
A total of 385 patients with endometrial cancer were included in the retrospective analysis who received primary surgical treatment between 2014 to 2018 and who had available pre-treatment CT scans. The AI-based tool was used to measure the L3 skeletal muscle area, waist skeletal muscle, visceral fat, and subcutaneous fat volume.
The low volumetric SMI group tended to be older and have lower BMI scores than the high volumetric SMI group. The SMI low group also had a higher proportion of patients with high-grade disease vs the SMI-high group (34.9% vs. 20.2%; P = .001).
Findings from the study have the potential to impact clinical practice, investigators stated. For example, patients with low volumetric SMI who are at a high risk of recurrence and mortality might require greater attention and surveillance.
“Based on assessment results, physicians may prescribe oral or intravenous nutritional support and best symptomatic care,” the investigators wrote. “Physical exercise or training intervention may be recommended to increase skeletal muscle mass or prevent further muscle loss during treatment.”
The authors cited a number of limitations to their study, the most significant of which was that CT scans were not regularly performed as part of the pre-treatment workup for endometrial cancer in the study years, which could introduce selection bias. It is also possible that treatment affected sarcopenia, or that patients experienced changes in body composition during their treatment.
Aside from this, the investigators stated that the data make a convincing case for waist volumetric SMI based as a prognostic tool.
“Considering that CT scans are commonly obtained as part of diagnosis, routine artificial intelligence-based volumetric quantification of waist skeletal muscle appears feasible in patients with endometrial cancer,” the authors concluded.
Kim SI, Chung JY, Paik H, et al. Prognostic role of computed tomography-based, artificial intelligence-driven waist skeletal muscle volume in uterine endometrial carcinoma. Insights Imaging. 2021;12(1):192. doi:10.1186/s13244-021-01134-y