Abstract
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.
Highlights
Loss of skeletal muscle (SM) mass is an important consideration in oncologic patients as a key component of cancer-related malnutrition, sarcopenia and cachexia [1, 2]
The training and validation performance of Cohort 1 in terms of Dice score and network loss for the five cross validation (CV) are given in Figure S3 and Figure S4 respectively
We present an ensemble model of 2.5D convolutional neural network (CNN) to automatically segment skeletal muscle on low quality Computed Tomography (CT) acquired in PET/CT studies, and investigate its qualitative and quantitative accuracy and precision to measure SM area and detect sarcopenia in NSCLC patients
Summary
Loss of skeletal muscle (SM) mass is an important consideration in oncologic patients as a key component of cancer-related malnutrition, sarcopenia and cachexia [1, 2]. SM area at L3 normalized by patient height is commonly used as a surrogate marker of sarcopenia in cancer [9, 12] and as a component of recent diagnostic criteria for malnutrition and sarcopenia [2, 13]. This marker is known as the L3 skeletal muscle index (SMI). 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. This project aims to develop an automatic method to segment L3 muscle in CT scans
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