Amarasinghe Kaushalya C, Lopes Jamie, Beraldo Julian, Kiss Nicole, Bucknell Nicholas, Everitt Sarah, Jackson Price, Litchfield Cassandra, Denehy Linda, Blyth Benjamin J, Siva Shankar, MacManus Michael, Ball David, Li Jason, Hardcastle Nicholas
Bioinformatics Core Facility, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia.
Front Oncol. 2021 May 7;11:580806. doi: 10.3389/fonc.2021.580806. eCollection 2021.
Muscle 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.
Attenuation 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.
We 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%.
This 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.
肌肉萎缩（肌少症）与癌症患者的不良预后相关。早期识别肌少症有助于进行营养和运动干预。计算机断层扫描（CT）图像中第三腰椎（L3）切片处的横断面骨骼肌（SM）面积越来越多地用于评估身体成分并计算SM指数（SMI），这是一种经过验证的癌症肌少症替代标志物。手动分割SM需要多个步骤，这限制了其在常规临床实践中的应用。本项目旨在开发一种自动方法来分割CT扫描中的L3肌肉。
使用了来自两项前瞻性试验中患者的全身PET-CT扫描的衰减校正CT。训练集由66例接受根治性放疗的非小细胞肺癌（NSCLC）患者组成。来自第二项试验的另外42例接受根治性放化疗的NSCLC患者用于测试。每位患者在治疗前后的不同时间点进行了多次CT扫描（训练和验证集中有147次CT扫描，独立测试集中有116次CT扫描）。两名观察者根据阿尔伯塔协议手动分割L3椎体处的骨骼肌，作为真实标签。这包括由两名观察者分割的40张图像，以测量观察者间的差异。使用2.5D全卷积神经网络（U-Net）集成进行分割。U-Net的最后一层将像素分为肌肉和非肌肉区域进行二元分类。使用Dice分数和手动与自动轮廓之间骨骼肌面积的绝对百分比误差（APE）来计算模型性能。
我们使用5折交叉验证训练了五个2.5D U-Net，并使用它们预测测试集中的轮廓。该模型在独立测试集中的平均Dice分数为0.92，APE为3.1%。这分别与观察者间平均Dice和APE的0.96和2.9%相似。我们使用计算机生成的骨骼肌面积进一步量化了肌少症分类的性能。为了符合基于阿尔伯塔协议的肌少症临床诊断，该模型的敏感性为84%，特异性为95%。
这项工作展示了一种自动方法，可准确且可重复地分割L3处的骨骼肌面积。这是一种用于大规模或常规计算癌症患者骨骼肌面积的有效工具，可能适用于作为PET/CT研究一部分获取的用于癌症患者分期和监测的低质量CT。
