A system for evaluating magnetic resonance imaging of prostate cancer using patient-specific 3D printed molds

Abstract

We have developed a system for evaluating magnetic resonance imaging of prostate cancer, using patient-specific 3D printed molds to facilitate MR-histology correlation. Prior to radical prostatectomy a patient receives a multiparametric MRI, which an expert genitourinary radiologist uses to identify and contour regions suspicious for disease. The same MR series is used to generate a prostate contour, which is the basis for design of a patient-specific mold. The 3D printed mold contains a series of evenly spaced parallel slits, each of which corresponds to a known MRI slice. After surgery, the patient's specimen is enclosed within the mold, and all whole-mount levels are obtained simultaneously through use of a multi-bladed slicing device. The levels are then formalin fixed, processed, and delivered to an expert pathologist, who identifies and grades all lesions within the slides. Finally, the lesion contours are loaded into custom software, which elastically warps them to fit the MR prostate contour. The suspicious regions on MR can then be directly compared to lesions on histology. Furthermore, the false-negative and false-positive regions on MR can be retrospectively examined, with the ultimate goal of developing methods for improving the predictive accuracy of MRI. This work presents the details of our analysis method, following a patient from diagnosis through the MR-histology correlation process. For this patient MRI successfully predicted the presence of cancer, but true lesion volume and extent were underestimated. Most cancer-positive regions missed on MR were observed to have patterns of low T2 signal, suggesting that there is potential to improve sensitivity.

Keywords: 3D printing; MRI; pathology; prostate cancer; registration; whole mount.

Figure 1

Region of suspicion on T2-weighted (left), K_trans (middle), and DWI (right) imaging.

Figure 2

Left: The patient’s 3D prostate surface (A) and the contours used to generate it in transverse (B), sagittal (C), and coronal (D) views. Right: A patient-specific 3D printed mold with parallel slits and a prostate cavity in the inner surface.

Figure 3

Illustration of the grossing process. An inked and trimmed prostate was placed within the patient-specific mold (1). Then the multi-bladed slicing device was used to acquire thin parallel whole-mount levels (2), which were removed from the mold (3) and labeled (4). These levels were then processed into slides, which were examined and annotated by an expert pathologist (5). The patient in this case had two apical slides showing cancer, with two distinct Gleason 3+3 = 6 lesions.

Figure 4

Illustration of elastic registration and the resulting warped contours. The MR prostate contour (top left) and the histology contour (top right) were superimposed using an affine transform based on centroids. The histological prostate surface was then warped (middle) to fit the MR contour, with arrows illustrating the effect of the warp. The bottom image shows the elastically warped histology targets, now in MR image space.

Figure 5

Coronal (A) and Sagittal (B & C) views of the MR prostate contour and 3D scan of the excised prostate surface. The MR prostate surface is seen in green. The excised prostate surface is seen in grey and red, before and after (respectively) removal of the seminal vesicles and shaving of the apex.

Figure 6

Lesion and ROI contours, progressing in 0.75 mm increments from midgland to apex.

Figure 7

3D profiles of the ROI and interpolated lesion volume.

Figure 8

Low T2 signal levels in image areas proven to be cancer-positive (A and B) and in areas assumed to be positive using interpolation between known lesions (C and D).

Figure 9

Illustration of the potential difficulty in distinguishing a true positive from a false positive region, with histology level (right) warped and co-registered to MR (left). Only the red regions were positive for cancer on histology, but no recognizable pattern was present to differentiate them from the green ROI on MRI.