Eight patients with histopathologically proven primary PCa (biopsy) received pre-therapeutic PSMA-PET/CT followed by radical prostatectomy (Table 1). The retrospective analysis was approved by the local ethics committee.
PET/CT
In vivo PET/CT scans were acquired either on a 64-channel GEMINI TF PET/CT or on a 16-channel GEMINI TF BIG BORE PET/CT (both Philips Healthcare, Cleveland, OH, PET pixel size x,y,z: 2 × 2 × 2 mm) which provide virtually identical image characteristics [12]. To ensure comparability of the measurements, the two scanners were cross-calibrated. At the time of the PET scan, a contrast-enhanced diagnostic CT (120 kVp, 100–400 mAs, dose modulation, pixel size x,y,z: 1.172 × 1.172 × 2 mm) was performed.
Ex vivo imaging and histopathology
After open radical retropubic prostatectomy and 24-h formalin fixation, the basic edges (ventral, dorsal, left, and right) of the resected prostate (see Fig. 1a) were marked with special ink to support orientation of the prostate in the agarose-filled cuvette and in the XYZ space of ex vivo CT. Radiopaque plastic pipes were inserted into the prostate for additional visual control between histopathologic slices and CT. The resected prostates were embedded in 6.5% agarose in a localizer with a 4-mm marker profile (see Fig. 1b).
The ex vivo CT scan was performed by means of planning CT for radiation oncology treatment (16-channel Phillips Brilliance Big Bore) using 120 kV and 100 mAs (pixel size x,y,z: 0,3 × 0,3 × 2 mm).
Every 4 mm, 2-μm-thick slices were cut at the same angle and position as the CT slices using a cutting device (Fig. 1c). Subsequently, the remaining pipes were removed from the slices, and a visible cavity remained which was still visible after histopathologic preparation. Pathologic work-up involves staining of the PCa with hematoxylin and eosin and delineation of PCa on every slice using black ink (Fig. 1d). Delineation was performed by an experienced pathologist manually supported by morphological patterns of healthy and malignant prostate tissue. Each slice was scanned with a CanoScan 9000F MarkII (Canon).
The histopathological information (see Fig. 1d, pink overlay) including tumor definition (black lines) was matched to the ex vivo CT scan. The contour of the prostate, the overlap between radiopaque pipes in CT and pipe cavities in histopathologic slices, and the markers at the localizer wall served as guidance for coregistration. VOIs were delineated within the ex vivo CT representing the PCa as well as the prostate where complete contours were estimated in case of resected prostate parts (Fig. 1e, red line).
Coregistration
The following main steps were performed:
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I.
Coregistration of histopathology and ex vivo CT including PCa delineation
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II.
Coregistration between in vivo and ex vivo CT scans
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III.
Modeling of 3D histoPET images based on the coregistered histopathology
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IV.
Coregistration procedures including PET information
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I.
Coregistration of histopathology and ex vivo CT including PCa delineation
In a manner similar to the procedure described by Grosu et al. [11], whole-mount prostate slices were coregistered to the ex vivo CT. In the current work, we used an improved fixation device (localizer) consisting of a customized cuvette with 4-mm-spaced markers, filled with agarose in which the prostate was embedded and fixated. After ex vivo CT scan of the localizer, the pathologic slices were cut perpendicular to the urethra and along the localizer markers using a customized cutting device. Thus, the sections obtained had the same cutting angle as the corresponding ex vivo CT slices (for detailed explanation see Fig. 1). Subsequently, ex vivo CT was displayed using the Medical Imaging Interaction Toolkit (MITK, [13]) and the entire prostatic gland was contoured. Matching between histology slices and ex vivo CT images was done visually in MITK. Once the coregistration of histology slices to ex vivo CT images was performed, the pathologic contours were transferred onto the CT images, and expanded by 2 mm in both Z axis directions to cover the volume in between the 4-mm-spaced histological cuts.
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II.
Coregistration between in vivo and ex vivo CT scans
Three methods of CT-based coregistration were compared:
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1.
Manual coregistration (ManReg)
Ex-vivo CT was manually (ManReg) coregistered to in vivo CT, using MITK software, based on a consensus of two independent observers. In the first step, the prostatic gland was delineated in the in vivo CT by using soft-tissue windowing (window level: 40–70 HU, window width: 100–200 HU). Ex vivo CT was oriented in the XYZ space of the in vivo CT by using the marker profile of the localizer. The axes between the apex and the prostatic base in ex−/ in vivo CT guided further coregistration, and rotation was used for final alignment. The delineated contours of the prostatic glands in ex vivo and in vivo CT served as reference points for anisotropic scaling of the ex vivo prostate, which was performed manually in all three dimensions. The transformations/deformations of the coregistration steps were also applied to the VOIs defined on the ex vivo CT in step I.
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2.
Manual coregistration with automatic scaling factor (ScalFactReg)
Manual coregistration as described in method 1, but with isotropic scaling of the ex vivo CT using a derived scaling factor to compensate for the prostate shrinking after prostatectomy. This scaling factor was calculated based on in vivo and ex vivo prostate volumes by:
\( \mathrm{scalingFactor}=\sqrt[3]{V_{\mathrm{vivo}}/{V}_{\mathrm{vitro}}} \),
where V
vivo is the volume of the whole prostate gland on in vivo CT and V
vitro is the volume of the delineation on the ex vivo CT.
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3.
Deformable coregistration (DefReg)
To cover non-affine deformations after prostatectomy, in vivo and ex vivo CT were coregistered by a deformable coregistration algorithm. As no internal structure of the prostate is visible on CT and the prominent drainage pipes on the ex vivo CT may lead to false correspondences an outline-based algorithm was chosen [14]. The algorithm simultaneously calculated correspondences and non-affine transformations between the outline points. Point correspondences were determined by so-called softassign, and the deformations by thin-plate spline method. As starting point, the delineations of in vivo and ex vivo CT were used and parameters were set to cover all possible point correspondences.
For the assessment of the performance and determination of spatial overlaps of CT-based coregistration approaches, the Dice Similarity Coefficient (DSC) was used: DSC = 2 ∨ A ∩ B ∨ (|A| + |B|). A represents the prostate contour done on the in vivo CT and B the prostate contour done on the ex vivo CT after coregistration.
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III.
Modeling of 3D histoPET images based on the coregistered histopathology
According to the PCa distribution established in step I, a value of 1 was assigned to every voxel classified as tumor volume, which may be interpreted as a histopathology-based tumor likelihood of 1. As the tracer PSMA binds to healthy prostate tissue as well, although to a much lesser extent, non-tumor voxels were set to 0.1. We estimated that the intra-tumor variability of PSMA accumulation is low in relation to the difference between healthy and malignant tissue, justifying a ‘binarized’ model within the prostate volume. Remaining voxels outside the prostate were assigned a value of 0.
To take into account the limited spatial resolution of PET (including the positron range of 68Ga, [15]) compared to histology, a Gaussian smoothing of the histological 3D information with an FWHM of 7 mm using the PMOD software package (version 3.6, PMOD Technologies Ltd.) was performed. This led to the so-called ‘histoPET’, corresponding to a modeled PET image implied by the given histopathologic tumor distribution (Fig. 2). The unit of these histoPET values was called ‘relSUV’ (in analogy to SUV in PET), which may be interpreted as smoothed tumor likelihood.
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IV.
Coregistration procedures including PET information
We added rigid mutual information (MI) coregistration of the in vivo PET scan and the modeled histoPET to our workflow. Each of the three CT-based coregistration procedures was used as starting point for matching histoPET and PSMA-PET/CT by MI where the results are named ManRegMI, ScalRegMI, and DefRegMI accordingly. MI was done with the “normalized mutual information” algorithm in PMOD v3.6 applying rigid transformations. For this purpose, masks were applied to the PET in order to take into account PET information only from the prostate. These masks were defined using anatomical CT information while sparing regions affected by high tracer accumulation in the bladder as visible in the PET image.
PET coregistration needed to be done carefully, since a reasonable agreement for simpler patterns can easily be found if the PETs are just shifted or rotated far enough. We estimated that shifts of up to two FWHMs of the PET resolution (i.e. 14 mm) can be considered still plausible. Measurement of transformations (shift/rotation) after MI coregistration was performed in PMOD. Furthermore, visual evaluation ensured that MI resulted in anatomical plausible transformations. The alignment of in vivo CT and PET scan was already given by the hardware coregistration of the combined PET/CT scanners.
Voxel-wise analysis
The spatial overlap between patterns of histoPET and PSMA-PET before and after MI was compared visually and quantitatively. PET signals from prostate regions which could not be examined histopathologically would bias the results. Thus, the statistical analysis needed to be derived from VOIs excluding such regions (similar to the histoPET-PET MI coregistration procedure, step IV in the coregistration workflow). Subsequently, SUV (PSMA-PET) and relSUV (histoPET) values for each voxel within the VOI were measured and linear regressions yielded coefficients of determination (R
2) as well as p values (t statistics, MATLAB R2014a) which were visualized by scatter plots.
