A novel approach for 3D reconstruction of the prostate gland that allows tumour location, volume estimation, and Gleason characterization
Oliveira, Pedro Meena, A. Bonthu, S. Singhal, N. Sachdeva, Ashwin Jain, Yatin Ramani, Vijay A C
Oliveira, Pedro
Meena, A.
Bonthu, S.
Singhal, N.
Sachdeva, Ashwin
Jain, Yatin
Ramani, Vijay A C
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Abstract
Introduction & Objectives: Improved identification of clinically significant PCa on mpMRI, would radically redefine the management of PCa.
Herein, we propose a novel approach based upon retrospective comparison of in vivo mpMRI images to spatially concordant digitally-scanned
post-prostatectomy H&E images. This comparison involves following steps: 1) localisation and Gleason grading of tumour foci; 2) reconstruction
of H&E slides in 3D; 3) alignment of reconstructed histology to mpMRI images; 4) labelling of aggressive prostate cancer on mpMRI images using
reconstructed 3D histology; 5) training a deep learning-based model for unsupervised segmentation of aggressive PCa foci using mpMRI images
and transferred labels. Using this approach, the extent of cancer can be mapped directly onto mpMRI enabling accurate segmentation of voxels
corresponding to tumour foci, including the identification of mpMRI-invisible lesions using radiomic features.
Materials & Methods: We present here the step 2 in the above mentioned approach. Whole-mount histopathological sections from totally
embedded radical prostatectomy specimens, with correspondent diagnostic pre-biopsy mpMRI, were used. Extreme apex and base tissue blocks
were cut perpendicular to the axial plane, with the central portion of the gland sliced in 4 mm thick sections. H&E whole-mount slides were digitised
at 40x magnification. 3D reconstruction was performed using a novel computational strategy that includes: 1) angular alignment of individual
macrodissected tissue pieces using colour ink markers; 2) scale alignment to fit the pieces on a pre-defined bounding box; 3) generation of
intermediate layers between two pieces; 4) normal vector estimation; and 5) Poisson reconstruction to generate the triangular mesh.
Results: The volume estimate from the original prostate specimen was compared to the reconstructed volume to assess the 3D reconstruction
performance. When tested on five radical prostatectomies, the method achieved a volume correlation of 85%-88%. Because the base and
apex portions are not discarded, we establish a high correlation between the reconstructed 3D histopathological volume and actual prostate
volume. Further, this methodology allowed identification not only of independent tumour foci within the gland but also 3D reconstruction of the
different Gleason patterns with accurate estimation of tumour volume and prognostic Grade Group.
Conclusions: A method for reconstructing 3D prostate volumes from 2D histology images has been presented. The development of radiomic and
deep learning algorithms to automatically detect prostate cancer on MRI will be aided by the accurate labelling of tumour foci on mpMRI images
using our 3D reconstruction approach.
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Oliveira PSO, Meena A, Bonthu S, Singhal N, Sachdeva A, Jain Y, et al. A novel approach for 3D reconstruction of the prostate gland that allows tumour location, volume estimation, and Gleason characterization. European Urology. 2022 Feb;81:S705-S. PubMed PMID: WOS:000812320400464.