Abstract
Purpose: The objective of this study was to establish an automated and objective method to quantitatively characterize the extent, spatial distribution, and temporal progression of metastatic disease in the bony spine.
Methods: Serial patient CT scans from GE Light-speed Plus CT Scanners were standardized to 120kVp, 1.25mm/2.5mm slice interval/ thickness, standard reconstruction, and 0.468mm/0.468mm pixel spacing. From 3D reconstructed CT images, trabecular regions within vertebral bodies (VBs) were segmented through atlas-based deformable registration (ITK, NLM, Bethesda). Voxel intensity histograms (voxel counts vs. Hounsfield Units) were used to characterize 32 healthy and 11 metastatically involved vertebrae (T5 to L5). Healthy histograms were fitted to Gaussian regression curves and compared using one-way repeated measures ANOVA (p< 0.05). Tumours were segmented as connected areas with voxel intensities between specified thresholds (Amira 3.1.1, TGS, Berlin).
Results: Histograms of healthy vertebrae were found to be Gaussian distributions (avg. RMSD = 30 voxel counts). The Gaussian mean & #956; ranged from 120 to 290HU, presumably due to inter-patient differences in age and activity. However, the histogram data sets were not significantly different (p> 0.8) across intra-patient vertebral levels T5-L5. Consequently, the Gaussian parameters, & #956; and standard deviation & #963;, determined from fitted healthy histograms could be used in adjacent metastatic levels to define patient-specific lytic and blastic thresholds for tumor segmentation. The ideal lytic and blastic segmentation thresholds were determined to be & #956;−& #963; and & #956;+2& #963; respectively: i.e. while histograms of metastatic VBs were non-Gaussian (RMSD of 56 voxels), subtracting from them the tumourous regions segmented accordingly restored the Gaussian nature of the distributions (RMSD of 24 voxels). Metastatic involvement can then be quantified from histograms of metastases in terms of: (1) lytic/ blastic volumes from areas under the curves; (2) severity of the pathologic involvement from the distribution and range; (3) tumor progression over time or treatment effects by taking the difference between sequential scans.
Conclusions: This proposed histogram-based method for characterizing spinal metastases shows great potential in extending the quantitative capacity of CT-based radiographic evaluations, especially in tracking meta-static progression and treatment effectiveness in clinical research applications. Funding: Other Education Grant Funding Parties: NSERC and CBCRA
Correspondence should be addressed to Cynthia Vezina, Communications Manager, COA, 4150-360 Ste. Catherine St. West, Westmount, QC H3Z 2Y5, Canada
