使用虚拟非钙双能计算机断层扫描检测股骨头坏死中的骨髓水肿
Detection of bone marrow edema in osteonecrosis of the femoral head using virtual noncalcium dual-energy computed tomography.
作者信息
Zuo Tianzi, Chen Yingmin, Zheng Hongming, Jia Xiuchuan, Bao Yunfeng, Wang Yuhang, Li Ling, Huang Xiaoying
机构信息
Department of Radiology, Hebei General Hospital, 348 Heping West Road, Shijiazhuang, 050000, Hebei, China.
Department of Nuclear Medicine, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, 050000, Hebei, China.
出版信息
Eur J Radiol. 2021 Jun;139:109681. doi: 10.1016/j.ejrad.2021.109681. Epub 2021 Mar 26.
PURPOSE
To determine the diagnostic performance of virtual noncalcium (VNCa) dual-energy computed tomography (DECT) in the detection of bone marrow edema (BME) in participants with osteonecrosis of the femoral head (ONFH).
METHODS
In this prospective study, 24 consecutive participants (15 men, 9 women; mean age, 44 years, range, 21-72 years) diagnosed with ONFH who underwent DECT and magnetic resonance imaging (MRI) between September 2019 and January 2020 were involved. Two independent readers visually evaluated color-coded VNCa images using a binary classification (0 = normal bone marrow, 1 = BME). MRI served as the reference standard for the presence of BME. Interobserver agreement for the visual evaluation of VNCa DECT images was calculated with κ statistics. We determined computed tomography (CT) numbers on VNCa images and weighted-average CT sets using region-of-interest-based quantitative analysis. The t-test was used to compare the differences of CT values between BME areas and normal bone marrow areas. Receiver operating characteristic (ROC) curve was used to select an optimal CT values of VNCa images for detecting BME. A p value of <0.05 was considered as statistically significant.
RESULTS
The sensitivity, specificity, and accuracy of Reader 1 and Reader 2, respectively, in the identification of BME at DECT were 95 % and 89 % (18 and 17 of 19), 96 % and 96 % (25 and 25 of 26), and 93 % (43 and 42 of 45). Interobserver agreement was excellent (κ = 0.86). The VNCa CT numbers of the BME area and the normal bone marrow area were -28.6 (-17.9--39.4) HU and -97.9 (-91.3--104.4) HU, respectively, with statistical significance (t = -10.6, p < 0.001). The weighted-average CT numbers of the BME area and the normal bone marrow area were 152.4(122.2-182.7) HU and 121.1(103.6-183.6) HU, respectively, with no statistical significance (t = -2.0, p > 0.05). The area under the receiver operating characteristic curve was 0.99 in differentiation of the BME from normal bone marrow. A cut-off value of -57.2 HU yielded overall sensitivity, specificity, and accuracy, respectively, of 95 % (18 of 19), 100 % (26 of 26), and 98 % (44 of 45) detection of BME in participants with ONFH.
CONCLUSION
Visual and quantitative analyses of VNCa images shows excellent diagnostic performance for assessing BME in participants with ONFH.
目的
确定虚拟去钙（VNCa）双能计算机断层扫描（DECT）在检测股骨头坏死（ONFH）患者骨髓水肿（BME）中的诊断性能。
方法
在这项前瞻性研究中，纳入了2019年9月至2020年1月期间连续24例被诊断为ONFH并接受DECT和磁共振成像（MRI）检查的患者（15例男性，9例女性；平均年龄44岁，范围21 - 72岁）。两名独立阅片者使用二元分类法（0 = 正常骨髓，1 = BME）对彩色编码的VNCa图像进行视觉评估。MRI作为BME存在与否的参考标准。使用κ统计量计算VNCa DECT图像视觉评估的观察者间一致性。我们使用基于感兴趣区域的定量分析确定VNCa图像上的计算机断层扫描（CT）值和加权平均CT集。采用t检验比较BME区域和正常骨髓区域CT值的差异。采用受试者操作特征（ROC）曲线选择VNCa图像检测BME的最佳CT值。p值<0.05被认为具有统计学意义。
结果
阅片者1和阅片者2在DECT上识别BME的敏感性、特异性和准确性分别为95%和89%（19例中的18例和17例）、96%和96%（26例中的25例和25例）以及93%（45例中的43例和42例）。观察者间一致性良好（κ = 0.86）。BME区域和正常骨髓区域的VNCa CT值分别为-28.6（-17.9 - -39.4）HU和-97.9（-91.3 - -104.4）HU，具有统计学意义（t = -10.6，p < 0.001）。BME区域和正常骨髓区域的加权平均CT值分别为152.4（122.2 - 182.7）HU和121.1（103.6 - 183.6）HU，无统计学意义（t = -2.0，p > 0.05）。在区分BME与正常骨髓时，受试者操作特征曲线下面积为0.99。截断值为-57.2 HU时，检测ONFH患者BME的总体敏感性、特异性和准确性分别为95%（19例中的18例）、100%（26例中的26例）和98%（45例中的44例）。
结论
VNCa图像的视觉和定量分析在评估ONFH患者的BME方面显示出优异的诊断性能。
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