Prognostic role of computed tomography-based, artificial intelligence-driven waist skeletal muscle volume in uterine endometrial carcinoma

Abstract

Objectives To investigate the impact of computed tomography (CT)-based, artificial intelligence-driven waist skeletal muscle volume on survival outcomes in patients with endometrial cancer. Methods We retrospectively identified endometrial cancer patients who received primary surgical treatment between 2014 and 2018 and whose pre-treatment CT scans were available (n = 385). Using an artificial intelligence-based tool, the skeletal muscle area (cm2) at the third lumbar vertebra (L3) and the skeletal muscle volume (cm3) at the waist level were measured. These values were converted to the L3 skeletal muscle index (SMI) and volumetric SMI by normalisation with body height. The relationships between L3, volumetric SMIs, and survival outcomes were evaluated. Results Setting 39.0 cm2/m2 of L3 SMI as cut-off value for sarcopenia, sarcopenia (< 39.0 cm2/m2, n = 177) and non-sarcopenia (≥ 39.0 cm2/m2, n = 208) groups showed similar progression-free survival (PFS; p = 0.335) and overall survival (OS; p = 0.241). Using the median value, the low-volumetric SMI group (< 206.0 cm3/m3, n = 192) showed significantly worse PFS (3-year survival rate, 77.3% vs. 88.8%; p = 0.004) and OS (3-year survival rate, 92.8% vs. 99.4%; p = 0.003) than the high-volumetric SMI group (≥ 206.0 cm3/m3, n = 193). In multivariate analyses adjusted for baseline body mass index and other factors, low-volumetric SMI was identified as an independent poor prognostic factor for PFS (adjusted HR, 1.762; 95% CI, 1.051–2.953; p = 0.032) and OS (adjusted HR, 5.964; 95% CI, 1.296–27.448; p = 0.022). Conclusions Waist skeletal muscle volume might be a novel prognostic biomarker in patients with endometrial cancer. Assessing body composition before treatment can provide important prognostic information for such patients.

Prognostic Value of Artificial Intelligence-Driven, Computed Tomography-Based, Volumetric Assessment of the Volume and Density of Muscle in Patients With Colon Cancer

“…Other studies on Asian patients reported that L3 sarcopenia, classified by the Q1 cutoff value, was an independent prognostic factor for worse DFS and OS in patients with colorectal cancer [ 22 24 ]. Only a few studies have reported that volumetric sarcopenia has a worse prognostic impact on gynecologic malignancies [ 8 9 29 ]. A single cross-sectional CT image at the L3 level, which reflects whole-body tissue quantities, is a well-established method for body composition analysis [ 27 ].…”

“…MD decreases with the worsening of myosteatosis [ 21 ]. The volumetric skeletal muscle index (SMI) was calculated as the waist muscle volume (cm 3 ) divided by height (m 3 ) [ 8 9 ]. Few studies have been conducted on volumetric parameters; therefore, the optimal cut-off values of SMI and MD for sarcopenia and myosteatosis were set as the Q1 values by referring to another study [ 8 ].…”

“…Malnutrition and changes in body composition, particularly muscle mass, have prognostic values; however, they tend to be neglected in the absence of recognized guidelines [ 4 7 ]. Sarcopenia, characterized by the loss of skeletal muscle mass and function, has recently been regarded as an objective risk factor for worse survival outcomes, higher surgical complications, and treatment-related toxicities for several cancer types [ 8 9 10 ]. In a meta-analysis, the prevalence of sarcopenia in patients with gastrointestinal cancer was 34.7% (range, 2.1%–83.3%) [ 11 ].…”

“…In addition, recent technological advances have enabled automated and fast volumetric measurements of each component of CT scans [ 8 15 16 ]. Automated volumetric measurements of body composition may contain more abundant and precise information than single-cut measurements in a cross-sectional image and can also enable the calculation of volumes in less than a few minutes [ 9 17 ]. This study aimed to evaluate the associations between the volume and density of skeletal muscles in the abdominal waist obtained from CT-based automated artificial intelligence (AI) volumetric measurements and survival outcomes of patients with colon cancer.…”

MAIC–10 brief quality checklist for publications using artificial intelligence and medical images

“…After excluding reviews and opinion letters, 33 original articles were identified. This list was further filtered and narrowed down to 10 relevant original articles dealing with AI and medical images developments [14][15][16][17][18][19][20][21][22][23]. The selected papers were critically assessed for compliance with each item included in the MAIC-10 checklist.…”