Department of Cardiothoracic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
North Allegheny Senior High School, Wexford, Pennsylvania, USA.
Med Phys. 2022 Nov;49(11):7108-7117. doi: 10.1002/mp.15821. Epub 2022 Jul 11.
Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging.
To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only.
A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R ) were used as the performance metrics to assess the model performance.
The R values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively.
Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.
从有限区域计算机断层扫描(CT)中估算全身成分在临床医学中有许多潜在的应用,但具有挑战性。
研究是否可以仅从胸部 CT 扫描中可靠地估算基于多种组织类型(内脏脂肪组织[VAT]、皮下脂肪组织[SAT]、肌间脂肪组织[IMAT]、骨骼肌[SM]和骨骼)的全身成分。
收集了我院接受胸部 CT 扫描和全身正电子发射断层扫描-CT 扫描的 97 例肺癌患者的队列。我们使用内部软件自动分割和量化 CT 图像中的 VAT、SAT、IMAT、SM 和骨骼。胸部 CT 扫描和全身 CT 扫描的视场被标准化,即分别从 T1 椎体到 L1 椎体和从 C1 椎体到底部骨盆。多元线性回归用于建立从胸部 CT 扫描估算全身组织体积的计算机模型。建模分析中包括受试者的人口统计学特征(如性别和年龄)和肺容积。十折交叉验证用于验证预测模型的性能。平均绝对差异(MAD)和 R 方(R )用作评估模型性能的性能指标。
从常规胸部 CT 扫描估算全身 SAT、VAT、IMAT、总脂肪、SM 和骨骼体积的 R 值分别为 0.901、0.929、0.900、0.933、0.928 和 0.918。相应的 MAD(百分比差异)分别为 1.44±1.21L(12.21%±11.70%)、0.63±0.49L(29.68%±61.99%)、0.12±0.09L(16.20%±18.42%)、1.65±1.40L(10.43%±10.79%)、0.71±0.68L(5.14%±4.75%)和 0.17±0.15L(4.32%±3.38%)。
我们的算法在从胸部 CT 扫描中估算全身成分的能力方面表现出良好的前景。基于胸部 CT 扫描的体成分测量比基于第三腰椎的体成分测量更准确。