Whole-Body Skeletal Muscle Assessment Using Lower Extremity CT Images.

EasyChair Preprint 15296

Abstract

Assessment of the loss in skeletal muscle mass (SMM) is often quantified by measuring the skeletal muscle index (SMI) through dual-energy x-ray absorptiometry (DXA). However, as SMI measurement is not always performed in the clinical setting, we aimed to develop and verify a system that predicts SMM loss from the CT images of the lower extremity that were acquired for the preoperative assessment of hip surgery. Sixty female patients with hip diseases who underwent CT and a whole-body DXA scan were retrospectively analyzed. Using an automated CT segmentation model of each muscle, muscles of the lower extremities were segmented and classified into three groups: gluteal muscles, muscles above the knee joint, and all lower extremity muscles. The relationship between the total muscle mass of the three groups and DXA-measured lean mass was assessed. Further, the SMI of the three groups was calculated, and their diagnostic performance in predicting sarcopenia (SMI < 5.4 kg/m^2) was evaluated using the receiver operating characteristic curve (ROC) analysis. Strong correlations were observed with lean mass and SMM of gluteal muscles (r = 0.82, p < 0.01), above the knee joint (r = 0.90, p < 0.01), and of all lower extremities (r = 0.94, p < 0.01). The area under the curves for each region were 0.93, 0.90, and 0.96, respectively. Our findings suggest that lower extremity CT scans with limited regions of interest may serve as a viable method for predicting whole-body SMM loss, indicating the possibility of diagnosing sarcopenia from such CT images.

Keyphrases: CT image, dual-energy x-ray absorptiometry, skeletal muscle

@booklet{EasyChair:15296,
  author    = {Sotaro Kono and Keisuke Uemura and Mazen Soufi and Makoto Iwasa and Kazuma Takashima and Hidetoshi Hamada and Yoshito Otake and Yoshinobu Sato and Seiji Okada and Nobuhiko Sugano},
  title     = {Whole-Body Skeletal Muscle Assessment Using Lower Extremity CT Images.},
  howpublished = {EasyChair Preprint 15296},
  year      = {EasyChair, 2024}}