Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Abstract

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinicfriendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.

Muscle mass change using linear measurement analysis after nephrectomy for pT3 and pT4 renal cell carcinoma is associated with mortality

“…Preoperative and postoperative axial CT or MRI studies were segmented at the mid-level of the third lumbar vertebrae, as done in previous studies. 16 The skeletal muscle surface area at this lumbar level has been found to be the strongest correlate of total skeletal muscle mass. 14 Linear segmentation was performed using the detailed protocol described by Avrutin et al 14 and was completed by two trained observers (E.M. and M.H.)…”

“…Preoperative and postoperative axial CT or MRI studies were segmented at the mid‐level of the third lumbar vertebrae, as done in previous studies 16 . The skeletal muscle surface area at this lumbar level has been found to be the strongest correlate of total skeletal muscle mass 14 .…”

“…[6][7][8][11][12][13] However, these studies fail to consider the dynamic nature of skeletal muscle change during cancer and its associated treatments, and how continuous degeneration or potential recovery may influence a patient's clinical trajectory. 13,16 Studies that have evaluated postoperative muscle restoration have demonstrated its prognostic capability, but by contrast, have been among patients undergoing extirpative surgery for malignancies other than those of renal origin, such as pancreatic, 7 periampullary, 8 non-small cell lung, 9 gastric, 10 and oesophageal 11 cancer. By contrast, to our knowledge, only one study thus far has investigated the prognostic effects of skeletal muscle recovery in surgical RCC patients.…”

“…It has been widely established that preoperative skeletal muscle deficiency is associated with adverse outcomes in patients with various malignancies, including RCC, bladder cancer, and urothelial carcinoma 6–8,11–13 . However, these studies fail to consider the dynamic nature of skeletal muscle change during cancer and its associated treatments, and how continuous degeneration or potential recovery may influence a patient's clinical trajectory 13,16 . Studies that have evaluated postoperative muscle restoration have demonstrated its prognostic capability, but by contrast, have been among patients undergoing extirpative surgery for malignancies other than those of renal origin, such as pancreatic, 7 periampullary, 8 non‐small cell lung, 9 gastric, 10 and oesophageal 11 cancer.…”

Body Composition as a Predictor of the Survival in Anal Cancer

“…All measurements were carried out on a single image at the intervertebral disc level between lumbar vertebrae L4-L5. For segmentation purposes, the following CT attenuation thresholds used were from 150 to −29 Hounsfield Unite (HU), from −50 to −150 HU, and from −30 to −190 HU for semi-automated segmentation of the total psoas muscle area (TPA), VAT, and SAT, respectively, using the masking tool in the program [ 17 ] ( Supplementary Figure S1 ).…”