Prostate magnetic resonance imaging (MRI) allows the detection and treatment planning of clinically significant cancers. However, indolent cancers, e.g., those with Gleason scores 3+3, are not readily distinguishable on MRI. Thus an image-guided biopsy is still required before proceeding with a radical treatment for aggressive tumors or considering active surveillance for indolent disease. The excision of the prostate as part of radical prostatectomy treatments provides a unique opportunity to correlate whole-mount histology slices with MRI. Through a careful spatial alignment of histology slices and MRI, the extent of aggressive and indolent disease can be mapped on MRI which allows one to investigate MRI-derived features that might be able to distinguish aggressive from indolent cancers. Here, we introduce a framework for the 3D spatial integration of radiology and pathology images in the prostate. Our approach, first, uses groupwise-registration methods to reconstruct the histology specimen prior to sectioning, and incorporates the MRI as a spatial constraint, and, then, performs a multi-modal 3D affine and deformable alignment between the reconstructed histology specimen and the MRI. We tested our approach on 15 studies and found a Dice similarity coefficient of 0.94±0.02 and a urethra deviation of 1.11±0.34 mm between the histology reconstruction and the MRI. Our robust framework successfully mapped the extent of disease from histology slices on MRI and created ground truth labels for characterizing aggressive and indolent disease on MRI.
KEYWORDS: Magnetic resonance imaging, X-rays, X-ray imaging, Breast cancer, Radiography, 3D image processing, 3D modeling, Image registration, Breast, Radiology
The widespread use of screening mammography has resulted in a remarkable rise in the diagnosis of Ductal Carcinoma In Situ (DCIS). A resultant challenge is the early screening of these patients to identify those with concurrent invasive breast cancer (IBC), as one in five DCIS at biopsy, are upgraded to IBC following surgery. Both x-ray mammography and multi-parametric Magnetic Resonance Imaging (MRI) lack the ability to distinguish DCIS from IBC reliably. Our robust methodology for 3D alignment of histopathology images and MRI provides a unique opportunity to spatially map digitized histopathology slides on pre-surgical MRI which is particularly important in the tumors where DCIS and IBC co-occur as well as for the study of tumor heterogeneity. In this proof-of-concept study, we developed and evaluated a methodological framework for the 3D spatial alignment of MRI and histopathology slices, using x-ray radiographs as intermediate modality. Our methodology involves (1) the co-registration of 2D x-ray radiographs showing macrosections and corresponding 2D histology slices, (2) the 3D reconstruction of the ex vivo specimen based on the x-ray images, and aligned histology slices, and (3) the registration of the 3D reconstructed ex vivo specimen with the 3D MRI. The spatially co-registered MRI and histopathology images may enable the identification of MRI features that distinguish aggressive from indolent disease on in vivo MRI.
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