Whyne Carl, Hardisty Michael, Wu Florence, Skrinskas Tomas, Clemons Mark, Gordon Lyle, Basran Parminder S
Orthopaedic Biomechanics Laboratory, Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Room UB-19, Toronto, Ontario M4N 3M5, Canada.
Med Phys. 2007 Aug;34(8):3279-85. doi: 10.1118/1.2756939.
Radiological imaging is essential to the appropriate management of patients with bone metastasis; however, there have been no widely accepted guidelines as to the optimal method for quantifying the potential impact of skeletal lesions or to evaluate response to treatment. The current inability to rapidly quantify the response of bone metastases excludes patients with cancer and bone disease from participating in clinical trials of many new treatments as these studies frequently require patients with so-called measurable disease. Computed tomography (CT) can provide excellent skeletal detail with a sensitivity for the diagnosis of bone metastases. The purpose of this study was to establish an objective method to quantitatively characterize disease in the bony spine using CT-based segmentations. It was hypothesized that histogram analysis of CT vertebral density distributions would enable standardized segmentation of tumor tissue and consequently allow quantification of disease in the metastatic spine. Thirty two healthy vertebral CT scans were first studied to establish a baseline characterization. The histograms of the trabecular centrums were found to be Gaussian distributions (average root-mean-square difference=30 voxel counts), as expected for a uniform material. Intrapatient vertebral level similarity was also observed as the means were not significantly different (p > 0.8). Thus, a patient-specific healthy vertebral body histogram is able to characterize healthy trabecular bone throughout that individual's thoracolumbar spine. Eleven metastatically involved vertebrae were analyzed to determine the characteristics of the lytic and blastic bone voxels relative to the healthy bone. Lytic and blastic tumors were segmented as connected areas with voxel intensities between specified thresholds. The tested thresholds were mu-1.0 sigma, mu - 1.5 sigma, and mu - 2.0 sigma, for lytic and mu + 2.0 sigma, mu+3.0 siema, and mu + 3.5 sigma for blastic tissue where mu and sigma were taken from the Gaussian characterization of a healthy level within the same patient. The ideal lytic and blastic segmentation thresholds were determined to be mu-sigma and mu + 2 sigma, respectively. Using the optimized thresholds to segment tumor tissue, a quantitative characterization of disease is possible to calculate tumor volumes, disease severity, and temporal progression or treatment effect. Our proposed histogram-based method for characterizing spinal metastases shows great potential in extending the quantitative capacity of CT-based radiographic evaluations.
放射影像学对于骨转移患者的合理治疗至关重要；然而，对于量化骨骼病变潜在影响的最佳方法或评估治疗反应，尚无广泛认可的指南。目前无法快速量化骨转移的反应，使得癌症和骨病患者无法参与许多新治疗的临床试验，因为这些研究通常要求患者患有所谓的可测量疾病。计算机断层扫描（CT）能够提供出色的骨骼细节，对骨转移的诊断具有敏感性。本研究的目的是建立一种基于CT分割的客观方法，以定量表征脊柱骨中的疾病。假设CT椎体密度分布的直方图分析能够实现肿瘤组织的标准化分割，从而对转移性脊柱中的疾病进行量化。首先对32例健康椎体CT扫描进行研究，以建立基线特征。如预期的均匀材料那样，发现小梁中心的直方图呈高斯分布（平均均方根差=30体素计数）。还观察到患者体内椎体水平的相似性，因为均值无显著差异（p>0.8）。因此，特定患者的健康椎体直方图能够表征该个体整个胸腰椎的健康小梁骨。对11个转移受累椎体进行分析，以确定相对于健康骨的溶骨性和成骨性骨体素的特征。溶骨性和成骨性肿瘤被分割为体素强度在指定阈值之间的相连区域。测试的阈值对于溶骨性组织为μ-1.0σ、μ-1.5σ和μ-2.0σ，对于成骨性组织为μ+2.0σ、μ+3.0σ和μ+3.5σ，其中μ和σ取自同一患者健康水平的高斯特征。确定理想的溶骨性和成骨性分割阈值分别为μ-σ和μ+2σ。使用优化的阈值分割肿瘤组织，可以对疾病进行定量表征，以计算肿瘤体积、疾病严重程度以及时间进展或治疗效果。我们提出的基于直方图的脊柱转移瘤表征方法在扩展基于CT的放射学评估的定量能力方面显示出巨大潜力。
