Volumetric Reconstruction of Prostatectomy Specimens from Histology

TL;DR

This paper introduces 3D-SLIVER, an open-source tool that simplifies volumetric reconstruction of prostatectomy specimens from histology, enhancing visualization, interdisciplinary communication, and research capabilities in prostate cancer diagnosis and treatment.

Contribution

The paper presents 3D-SLIVER, a user-friendly, open-source extension for 3D-Slicer that streamlines the process of 3D tissue reconstruction from histological slides.

Findings

Enables improved visualization of prostate specimens.

Facilitates retrospective analysis of diagnostic predictions.

Supports integration into clinical workflows and research.

Abstract

Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex information that is difficult to represent in reports, although it is of significant interest to the other medical specialties involved. 3D tissue reconstruction would allow for better spatial visualization, as well as combinations with other imaging modalities. Existing approaches in this area have proven labor-intensive and challenging to integrate into clinical workflows. 3D-SLIVER provides a simplified solution, implemented as an open-source 3DSlicer extension. We outline three specific real-world scenarios to illustrate its potential to improve transparency in diagnostic workflows and contribute to multi-modal research endeavors. Implementing the 3D reconstruction process involved four sub-modules of 3D-SLIVER: digitization of slicing protocol, virtual slicing of arbitrary 3D models based on that protocol, registration of slides with virtual slices using the Coherent Point Drift algorithm, and 3D reconstruction of registered information using convex hulls, Gaussian splatter and linear extrusion. Three use cases to employ 3D-SLIVER are presented: a low-effort approach to pathology workflow integration and two research-related use cases illustrating how to perform retrospective evaluations of PI-RADS predictions and statistically model 3D distributions of morphological patterns. 3D-SLIVER allows for improved interdisciplinary communication among specialties. It is designed for simplicity in application, allowing for flexible integration into various workflows and use cases. Here we focused on the clinical care of prostate cancer patients, but future possibilities are extensive with other neoplasms and in education and research.