MP58-10 A NOVEL APPROACH FOR 3D RECONSTRUCTION OF THE PROSTATE GLAND THAT ALLOWS TUMOUR LOCATION, VOLUME ESTIMATION, AND GLEASON CHARACTERIZATION

Abstract

You have accessJournal of UrologyCME1 May 2022MP58-10 A NOVEL APPROACH FOR 3D RECONSTRUCTION OF THE PROSTATE GLAND THAT ALLOWS TUMOUR LOCATION, VOLUME ESTIMATION, AND GLEASON CHARACTERIZATION Pedro Oliveira, Avaneesh Meena, Saikiran Bonthu, Nitin Singhal, Ashwin Sachdeva, Yatin Jain, and Vijay Ramani Pedro OliveiraPedro Oliveira More articles by this author , Avaneesh MeenaAvaneesh Meena More articles by this author , Saikiran BonthuSaikiran Bonthu More articles by this author , Nitin SinghalNitin Singhal More articles by this author , Ashwin SachdevaAshwin Sachdeva More articles by this author , Yatin JainYatin Jain More articles by this author , and Vijay RamaniVijay Ramani More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002641.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We propose a novel approach for identification (ID) of clinical significant PCa on mpMRI based upon retrospective comparison of in vivo mpMRI images to spatially concordant digitally-scanned post-prostatectomy H&E images. 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 ID of mpMRI-invisible lesions using radiomic features. METHODS: The step 2 is presented herein. Whole-mount histopathological sections from totally embedded radical prostatectomy specimens, with correspondent diagnostic pre-biopsy mpMRI, were used. 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. 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 with a 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 ID 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. Source of Funding: NHS/Aira-Matrix Project PO01 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e995 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Pedro Oliveira More articles by this author Avaneesh Meena More articles by this author Saikiran Bonthu More articles by this author Nitin Singhal More articles by this author Ashwin Sachdeva More articles by this author Yatin Jain More articles by this author Vijay Ramani More articles by this author Expand All Advertisement PDF DownloadLoading ...