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Open Access 01-12-2024 | Artificial Intelligence | Research

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

Authors: Lewei Duan, Zheng Liu, Fangning Wan, Bo Dai

Published in: BMC Cancer | Issue 1/2024

Abstract

Background

Whole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.

Methods

An extensive PubMed search was conducted until February 26, 2023, with the search term “prostate”, “whole-mount”, “large format histology”, which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.

Results

WMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.

Conclusions

WMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

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Title: Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects
Authors: Lewei Duan
Zheng Liu
Fangning Wan
Bo Dai
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Artificial Intelligence
Prostate Cancer
Prostate Cancer
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Positron Emission Tomography
Published in: BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-024-12071-6

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