Abstract
BackgroundImplementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. Direct visual comparison of PET/CT slices to whole prostate sections is hampered by considerable uncertainties from imperfect coregistration and fundamentally different image modalities. In the current study, we present a novel method for advanced voxel-wise comparison of histopathology from excised prostates to pre-surgical PET. Resected prostates from eight patients who underwent PSMA-PET/CT were scanned (ex vivo CT) and thoroughly pathologically prepared. In vivo and ex vivo CT including histopathology were coregistered with three different methods (manual, semi−/automatic). Spatial overlap after CT-based registration was evaluated with dice similarity (DSC). Furthermore, we constructed 3D cancer distribution models from histopathologic information in various slices. Subsequent smoothing reflected the intrinsically limited spatial resolution of PSMA-PET. The resulting histoPET models were used for quantitative analysis of spatial histopathology-PET pattern agreement focusing on p values and coefficients of determination (R2). We examined additional rigid mutual information (MI) coregistration directly based on PSMA-PET and histoPET.ResultsMean DSC for the three different methods (ManReg, ScalFactReg, and DefReg) were 0.79 ± 0.06, 0.82 ± 0.04, and 0.90 ± 0.02, respectively, while quantification of PET-histopathology pattern agreement after CT-based registration revealed R2 45.7, 43.2, and 41.3% on average with p < 10−5. Subsequent PET-based MI coregistration yielded R2 61.3, 55.9, and 55.6%, respectively, while implying anatomically plausible transformations.ConclusionsCreating 3D histoPET models based on thorough histopathological preparation allowed sophisticated quantitative analyses showing highly significant correlations between histopathology and (PSMA-)PET. We recommend manual CT-based coregistration followed by a PET-based MI algorithm to overcome limitations of purely CT-based coregistrations for meaningful voxel-wise comparisons between PET and histopathology.
Highlights
Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation
Starting after prostatectomy with a procedure similar to that described by Grosu et al [11] to match the histopathologic specimen with ex vivo CTs, we introduce modeling of a 3D histopathology dataset taking into account the physical properties of PET
Wilcoxon matched-pairs signedrank test showed no significant difference between Manual coregistration (ManReg) and ScalFactReg (p = 0.219), whereas Dice Similarity Coefficient (DSC) were significant higher after Deformable coregistration (DefReg) compared to ManReg (p = 0.008) and ScalFactReg (p = 0.008), respectively
Summary
Implementation of PET/CT in diagnosis of primary prostate cancer (PCa) requires a profound knowledge about the tracer, preferably from a quantitative evaluation. For introduction of a new PET tracer in diagnosis and treatment planning of primary PCa, profound knowledge about tracer accumulation in PCa and non-PCa tissue is necessary. This can be achieved only by voxel-wise examination of the tracer’s performance within the prostate. A difference in the resolution of the available information between PET/CT imaging (resolution and slice thickness in millimeters) and histopathology (planar resolution in microns but highly incomplete axial sampling) induces further challenges in terms of coregistration [4] as well as of interpretation
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