Dataset Information

Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults.

ABSTRACT: OBJECTIVE:Area of muscle, fat, and bone is often measured in thigh CT scans when tissue composition is a key outcome. SliceOmatic software is commonly referenced for such analysis but published methods may be insufficient for new users. Thus, a quick start guide to calculating thigh composition using SliceOmatic has been developed. METHODS:CT images of the thigh were collected from older (69 ± 4 yrs, N = 24) adults before and after 12-weeks of resistance training. SliceOmatic was used to segment images into seven density regions encompassing fat, muscle, and bone from -190 to +2000 Hounsfield Units [HU]. The relative contributions to thigh area and the effects of tissue density overlap for skin and marrow with muscle and fat were determined. RESULTS:The largest contributors to the thigh were normal fat (-190 to -30 HU, 29.1 ± 7.4%) and muscle (35 to 100 HU, 48.9 ± 8.2%) while the smallest were high density (101 to 150 HU, 0.79 ± 0.50%) and very high density muscle (151 to 200 HU, 0.07 ± 0.02%). Training significantly (P<0.05) increased area for muscle in the very low (-29 to -1 HU, 5.5 ± 7.9%), low (0 to 34 HU, 9.6 ± 16.8%), normal (35 to 100 HU, 4.2 ± 7.9%), and high (100 to 150 HU, 70.9 ± 80.6%) density ranges for muscle. Normal fat, very high density muscle and bone did not change (P>0.05). Contributions to area were altered by ~1% or less and the results of training were not affected by accounting for skin and marrow. CONCLUSIONS:When using SliceOmatic to calculate thigh composition, accounting for skin and marrow may not be necessary. We recommend defining muscle as -29 to +200 HU but that smaller ranges (e.g. low density muscle, 0 to 34 HU) can easily be examined for relationships with the health condition and intervention of interest. TRIAL REGISTRATION:Clinicaltrials.gov NCT02261961.

SUBMITTER: Dennis RA

PROVIDER: S-EPMC6168133 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults.

Dennis Richard A RA Long Douglas E DE Landes Reid D RD Padala Kalpana P KP Padala Prasad R PR Garner Kimberly K KK Wise James N JN Peterson Charlotte A CA Sullivan Dennis H DH

PloS one 20181002 10

<h4>Objective</h4>Area of muscle, fat, and bone is often measured in thigh CT scans when tissue composition is a key outcome. SliceOmatic software is commonly referenced for such analysis but published methods may be insufficient for new users. Thus, a quick start guide to calculating thigh composition using SliceOmatic has been developed.<h4>Methods</h4>CT images of the thigh were collected from older (69 ± 4 yrs, N = 24) adults before and after 12-weeks of resistance training. SliceOmatic was ...[more]

PMID: 30278056

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Project description:BackgroundMuscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans.MethodsAttenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours.ResultsWe trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%.ConclusionsThis work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.

Project description:BACKGROUND:Current literature is inconsistent in the associations between computed tomography (CT)-based body composition measures and adverse outcomes in older patients with colorectal cancer (CRC). Moreover, the associations with consecutive treatment modalities have not been studied. This study compared the associations of CT-based body composition measures with surgery- and chemotherapy-related complications and survival in older patients with CRC. MATERIALS AND METHODS:A retrospective single-center cohort study was conducted in patients with CRC aged ?65?years who underwent elective surgery between 2010 and 2014. Gender-specific standardized scores of preoperative CT-based skeletal muscle (SM), muscle density, intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), subcutaneous adipose tissue, IMAT percentage, SM/VAT, and body mass index (BMI) were tested for their associations with severe postoperative complications, prolonged length of stay (LOS), readmission, and dose-limiting toxicity using logistic regression and 1-year and long-term survival (range 3.7-6.6?years) using Cox regression. Bonferroni correction was applied to account for multiple testing. RESULTS:The study population consisted of 378 patients with CRC with a median age of 73.4 (interquartile range 69.5-78.4) years. Severe postoperative complications occurred in 13.0%, and 39.4% of patients died during follow-up. Dose-limiting toxicity occurred in 77.4% of patients receiving chemotherapy (n =?53). SM, muscle density, VAT, SM/VAT, and BMI were associated with surgery-related complications, and muscle density, IMAT, IMAT percentage, and SM/VAT were associated with long-term survival. After Bonferroni correction, no CT-based body composition measure was significantly associated with adverse outcomes. Higher BMI was associated with prolonged LOS. CONCLUSION:The associations between CT-based body composition measures and adverse outcomes of consecutive treatment modalities in older patients with CRC were not consistent or statistically significant. IMPLICATIONS FOR PRACTICE:Computed tomography (CT)-based body composition, including muscle mass, muscle density, and intermuscular, visceral, and subcutaneous adipose tissue, showed inconsistent and nonsignificant associations with surgery-related complications, dose-limiting toxicity, and overall survival in older adults with colorectal cancer. This study underscores the need to verify whether CT-based body composition measures are worth implementing in clinical practice.

Dataset Information

Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults.

Publications

Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults.

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