Improved estimates of strength and stiffness in pathologic vertebrae with bone metastases using CT-derived bone density compared with radiographic bone lesion quality classification.
Improved estimates of strength and stiffness in pathologic vertebrae with bone metastases using CT-derived bone density compared with radiographic bone lesion quality classification.
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DOI：
10.3171/2021.2.spine202027
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发表时间：
2022-01-01
影响因子：
2.8
通讯作者：
Hackney, David B.
中科院分区：
文献类型：
--
作者：
Alkalay, Ron N.;Groff, Michael W.;Stadelmann, Marc A.;Buck, Florian M.;Hoppe, Sven;Theumann, Nicolas;Mektar, Umesh;Davis, Roger B.;Hackney, David B.
关键词：
本研究的目的是比较1）CT衍生的骨病变质量（椎体骨转移[BM]的分类）和2）计算的CT测量的体积骨矿物质密度（vBMD）评价转移性脊柱疾病供体尸体椎体强度和刚度的能力。从11名患有乳腺癌、食管癌、肾癌、肺癌或前列腺癌的供体的尸体脊柱中获得45个胸椎和腰椎。使用microCT（21.4 μm）对每个椎骨进行成像，计算vBMD和骨体积与总体积的比值，并通过实验测量抗压强度和刚度。以1 mm体素大小重建microCT图像，以模拟轴向和矢状临床CT图像。5名临床专家根据骨病变质量（溶骨性、成骨性、混合性或健康）对图像进行盲分类。Fleiss' kappa检验用于检验5名临床评定者对骨病变质量分类的一致性。使用Kruskal-Wallis ANOVA检验基于骨病变质量的椎体强度和刚度差异。多变量回归分析用于检验骨病变质量、计算的vBMD、年龄、性别和种族对预测椎体强度和刚度的独立贡献。发现骨病变质量的评估者间一致性较低（κ = 0.19）。尽管成骨细胞椎体的强度显著高于溶骨性椎体（p = 0.0148），但多变量分析显示，骨病变质量解释了椎体强度变异性的19%和椎体刚度变异性的13%。计算的vBMD解释了75%的椎体强度（p < 0.0001）和48%的刚度（p < 0.0001）变异性。BM的类型影响了基于vBMD的椎体强度估计，在成骨细胞椎体中解释了75%的强度变异性（R2 = 0.75，p < 0.0001），但在混合骨转移椎体中仅解释了41%（R2 = 0.41，p = 0.0168），在溶骨性椎体中仅解释了39%（R2 = 0.39，p = 0.0381）。对于椎体刚度，vBMD仅与成骨细胞椎体相关（R2 = 0.44，p = 0.0024）。年龄和种族不一致地影响模型的强度和刚度预测。病理性椎体骨折发生在转移性病变降低椎体强度时，使其无法承受日常负荷。本研究证实了骨病变质量的定性临床分类预测病理性椎体强度和刚度的局限性。计算CT衍生的vBMD更可靠地估计椎体强度和刚度。用计算的vBMD估计值代替定性临床分类可能会改善椎骨骨折风险的预测。
The aim of this study was to compare the ability of 1) CT-derived bone lesion quality (classification of vertebral bone metastases [BM]) and 2) computed CT-measured volumetric bone mineral density (vBMD) for evaluating the strength and stiffness of cadaver vertebrae from donors with metastatic spinal disease. Forty-five thoracic and lumbar vertebrae were obtained from cadaver spines of 11 donors with breast, esophageal, kidney, lung, or prostate cancer. Each vertebra was imaged using microCT (21.4 μm), vBMD, and bone volume to total volume were computed, and compressive strength and stiffness experimentally measured. The microCT images were reconstructed at 1-mm voxel size to simulate axial and sagittal clinical CT images. Five expert clinicians blindly classified the images according to bone lesion quality (osteolytic, osteoblastic, mixed, or healthy). Fleiss’ kappa test was used to test agreement among 5 clinical raters for classifying bone lesion quality. Kruskal-Wallis ANOVA was used to test the difference in vertebral strength and stiffness based on bone lesion quality. Multivariable regression analysis was used to test the independent contribution of bone lesion quality, computed vBMD, age, gender, and race for predicting vertebral strength and stiffness. A low interrater agreement was found for bone lesion quality (κ = 0.19). Although the osteoblastic vertebrae showed significantly higher strength than osteolytic vertebrae (p = 0.0148), the multivariable analysis showed that bone lesion quality explained 19% of the variability in vertebral strength and 13% in vertebral stiffness. The computed vBMD explained 75% of vertebral strength (p < 0.0001) and 48% of stiffness (p < 0.0001) variability. The type of BM affected vBMD-based estimates of vertebral strength, explaining 75% of strength variability in osteoblastic vertebrae (R2 = 0.75, p < 0.0001) but only 41% in vertebrae with mixed bone metastasis (R2 = 0.41, p = 0.0168), and 39% in osteolytic vertebrae (R2 = 0.39, p = 0.0381). For vertebral stiffness, vBMD was only associated with that of osteoblastic vertebrae (R2 = 0.44, p = 0.0024). Age and race inconsistently affected the model’s strength and stiffness predictions. Pathologic vertebral fracture occurs when the metastatic lesion degrades vertebral strength, rendering it unable to carry daily loads. This study demonstrated the limitation of qualitative clinical classification of bone lesion quality for predicting pathologic vertebral strength and stiffness. Computed CT-derived vBMD more reliably estimated vertebral strength and stiffness. Replacing the qualitative clinical classification with computed vBMD estimates may improve the prediction of vertebral fracture risk.