目的:评估肩部CT扫描所见骨骼的计算机断层扫描(CT)衰减是否可用于预测低骨密度(BMD)(骨质减少/骨质疏松症),并比较两种机器学习模型预测低BMD的性能。
方法:194名年龄在50岁或以上(69.2+/-9.1岁;170名女性)的患者在1月1日之间的1年内接受了未增强的肩部CT扫描和双能X线吸收法(DXA),2010年12月31日,2021年进行了评估。肱骨的CT衰减,关节盂,喙突,肩峰,锁骨,首先,第二,第三根肋骨使用3D切片器获得。使用支持向量机(SVM)和k最近邻(kNN)来预测低BMD。使用DeLong检验比较曲线下面积(AUC)。
结果:锁骨195.4Hounsfield单位(HU)的CT衰减对预测低BMD的敏感性为0.577,特异性为0.781,AUC为0.701。在测试数据集中,SVM的敏感性为0.686,特异性为1.00,AUC为0.857;而kNN模型的敏感性为0.966,特异性为0.200,AUC为0.583。支持向量机对锁骨的CT衰减率优于(P=0.003),但不优于kNN模型(P=0.098)。
结论:锁骨的CT衰减最适合预测低BMD,然而,多变量支持向量机对预测低BMD有优势。
结论:利用许多部位的CT衰减的SVM对于预测低BMD是最好的。
OBJECTIVE: To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD.
METHODS: In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third
ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs).
RESULTS: A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098).
CONCLUSIONS: The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD.
CONCLUSIONS: SVM utilizing the CT attenuations at many sites was best for predicting low BMD.