改良腹部常规双能 CT 数据与 DXA 扫描之间骨密度值相关性的模型。
Model for improved correlation of BMD values between abdominal routine Dual energy CT data and DXA scans.
机构信息
Department of Radiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Department of Endocrinology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
出版信息
Eur J Radiol. 2018 Feb;99:76-81. doi: 10.1016/j.ejrad.2017.12.017. Epub 2017 Dec 23.
BACKGROUND
Osteoporosis is a common but underdiagnosed and undertreated disease causing severe morbidity and economic burden. The gold standard for detection of osteoporosis is DXA (dual energy x-ray absorptiometry), which is a dedicated examination for osteoporosis. Dual energy CT (DECT) examinations are increasingly used in daily routine for a wide variety of diagnoses. In the present study, we wanted to examine whether vBMD (volume bone mass density) could be evaluated as a side product in non-contrast as well as contrast phases as well as to evaluate a correction model taking known shortcomings for DXA into account.
METHODS
A total of 20 patients, i.e. 79 vertebrae (one excluded due to vertebral fracture), mean age 71 years (range 43-85) with a mean BMI (body mass index) of 26 (range 17-33) were examined with both abdominal/pelvic DECT as well as DXA. Furthermore, aortic calcium was measured as well as the presence of osteoarthritis of the spine (OAS) and osteoarthritis in facet joints (OAF) with a 5-grade scaling system.
RESULTS
A significant correlation was found between DXA BMD and vBMD from DECT with no contrast (WNC) (r = 0.424, p = 0.001), and with venous contrast (WVC) (r = 0.402, p < 0.001), but no significant correlation was found with arterial contrast (WAC). Using multivariate linear regression with DXA BMD as dependent, two models were created combining DECT WNC, aortic calciumscore (ACS), OAS and BMI yielding an R = 0.616 (model 1) and replacement of WNC to WVC a R = 0.612 (model 2). The Pearson correlation between DXA and predictive DXA BMD value of model 1 was r = 0.785 (p < 0.001) and model 2 r = 0.782 (p < 0.001).
CONCLUSION
There is a correlation between DXA BMD and DECT in non-contrast and venous contrast scans but not in arterial scans. The correlation is further improved by quantifying the degree of different confounding factors (osteoarthritis of the spine, body mass index and aortic calcium score) and taking these into account in an explanatory model. Future software solutions with DECT data as input data might be able to automatically measure the BMD in the trabecular bone as well as measuring the confounding factors automatically in order to obtain spinal DXA comparable BMD values.
背景
骨质疏松症是一种常见但诊断不足和治疗不足的疾病，会导致严重的发病率和经济负担。骨质疏松症的金标准检测方法是 DXA（双能 X 射线吸收法），这是一种专门用于检测骨质疏松症的检查方法。双能 CT（DECT）检查在日常工作中越来越多地用于各种诊断。在本研究中，我们想检查非对比和对比相是否可以作为侧产物评估 vBMD（体积骨密度），并评估一个考虑到 DXA 已知缺点的校正模型。
方法
总共检查了 20 名患者，即 79 个椎体（因椎体骨折而排除 1 个），平均年龄 71 岁（范围 43-85 岁），平均 BMI（体重指数）为 26（范围 17-33），同时进行腹部/骨盆 DECT 和 DXA 检查。此外，还测量了主动脉钙含量，以及脊柱骨关节炎（OAS）和小关节骨关节炎（OAF）的存在程度，采用 5 级评分系统进行评估。
结果
DXA BMD 与 DECT 无对比（WNC）（r=0.424，p=0.001）和静脉对比（WVC）（r=0.402，p<0.001）之间存在显著相关性，但与动脉对比（WAC）无显著相关性。使用 DXA BMD 为因变量的多元线性回归，创建了两个模型，将 DECT WNC、主动脉钙评分（ACS）、OAS 和 BMI 结合起来，得到 R=0.616（模型 1）和将 WNC 替换为 WVC，得到 R=0.612（模型 2）。模型 1 的 DXA 和预测 DXA BMD 值的 Pearson 相关系数为 r=0.785（p<0.001），模型 2 的 r=0.782（p<0.001）。
结论
DXA BMD 与非对比和静脉对比扫描的 DECT 之间存在相关性，但与动脉扫描无相关性。通过量化不同混杂因素（脊柱骨关节炎、体重指数和主动脉钙评分）的程度，并在解释模型中考虑这些因素，相关性得到进一步提高。未来的软件解决方案可以使用 DECT 数据作为输入数据，自动测量小梁骨的 BMD，并自动测量混杂因素，以获得与脊柱 DXA 相媲美的 BMD 值。
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