Department of Radiology, Mayo Clinic, Jacksonville, FL 32224; Center for Augmented Intelligence, Mayo Clinic, Jacksonville, FL 32224.
Department of Radiology, Mayo Clinic, Jacksonville, FL 32224.
J Neuroradiol. 2023 May;50(3):293-301. doi: 10.1016/j.neurad.2022.08.001. Epub 2022 Aug 27.
Computed Tomography (CT) scans of the cervical spine are often performed to evaluate patients for trauma and degenerative changes of the cervical spine. We hypothesized that the CT attenuation of the cervical vertebrae can be used to identify patients who should be screened for osteoporosis.
A retrospective study of 253 patients (177 training/validation and 76 test) with unenhanced CT scans of the cervical spine and Dual-energy x-ray Absorbtiometry (DXA) studies within 12 months of each other was performed. Volumetric segmentation of C1-T1, clivus, and first ribs was performed to obtain the CT attenuation of each bone. The correlations of the CT attenuations between the bones and with DXA measurements were evaluated. Univariate receiver operator characteristic (ROC) analyses, and multivariate classifiers (Random Forest (RF), XGBoost, Naïve Bayes (NB), and Support Vector Machines (SVM)) analyzing the CT attenuation of all bones, were utilized to predict patients with osteopenia/osteoporosis and femoral neck bone mineral density (BMD) T-scores <-1.
There were positive correlations between the CT attenuation of each bone, and with the DXA measurements. A CT attenuation threshold of 305.2 Hounsfield Units (HU) at C3 had the highest accuracy (0.763, AUC=0.814) to detect femoral neck BMD T-scores ≤-1 and a CT attenuation threshold of 323.6 HU at C3 had the highest accuracy (0.774, AUC=0.843) to detect osteopenia/osteoporosis. The SVM classifier (AUC=0.756) had higher AUC than the RF (AUC=0.692, P=0.224), XGBoost (AUC=0.736; P=0.814), NB (AUC=0.622, P=0.133) and CT threshold of 305.2 HU at C3 (AUC=0.704, P=0.531) classifiers to identify patients with femoral neck BMD T-scores <-1. The SVM classifier (accuracy=0.816) was more accurate than using the CT threshold of 305.2 HU at C3 (accuracy=0.671) (McNemar's χ=7.55, P=0.006).
Opportunistic screening for low BMD can be done using cervical spine CT scans. A SVM classifier was more accurate than using the CT threshold of 305.2 HU at C3.
颈椎计算机断层扫描(CT)常用于评估颈椎创伤和退行性改变。我们假设颈椎椎体的 CT 衰减值可用于识别需要进行骨质疏松筛查的患者。
对 253 例患者(177 例训练/验证和 76 例测试)进行回顾性研究,这些患者在 12 个月内均接受了颈椎未增强 CT 扫描和双能 X 射线吸收法(DXA)检查。对 C1-T1、枢椎和第一肋骨进行容积分割,以获得每个骨骼的 CT 衰减值。评估骨骼之间和与 DXA 测量值的 CT 衰减相关性。利用单变量接受者操作特征(ROC)分析和多变量分类器(随机森林(RF)、XGBoost、朴素贝叶斯(NB)和支持向量机(SVM))分析所有骨骼的 CT 衰减值,预测骨质疏松症/骨质疏松症和股骨颈骨密度(BMD)T 评分<-1 的患者。
各骨骼的 CT 衰减值与 DXA 测量值之间存在正相关。C3 处的 CT 衰减阈值为 305.2 亨氏单位(HU)具有最高的准确性(0.763,AUC=0.814),可检测股骨颈 BMD T 评分≤-1,C3 处的 CT 衰减阈值为 323.6 HU 具有最高的准确性(0.774,AUC=0.843),可检测骨质疏松症/骨质疏松症。SVM 分类器(AUC=0.756)的 AUC 高于 RF(AUC=0.692,P=0.224)、XGBoost(AUC=0.736;P=0.814)、NB(AUC=0.622,P=0.133)和 C3 处的 CT 衰减阈值 305.2 HU(AUC=0.704,P=0.531)分类器,以识别股骨颈 BMD T 评分<-1 的患者。SVM 分类器(准确率=0.816)比使用 C3 处的 CT 衰减阈值 305.2 HU(准确率=0.671)更准确(McNemar χ=7.55,P=0.006)。
可以使用颈椎 CT 扫描进行低 BMD 的机会性筛查。SVM 分类器比使用 C3 处的 CT 衰减阈值 305.2 HU 更准确。