An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice

Markus Bauer, Lennart Schneider, Marit Bernhardt, Christoph Augenstein, Glen Kristiansen, Bogdan Franczyk, Bogdan Franczyk

2024

Abstract

The histopathological analysis of prostate tissue is challenging due to the required expertise and the inherently high number of samples. This accounts especially for prostate cancer (PCa) assessment (tumour grading), as parameters like the Gleason score have high prognostic relevance, but suffer from significant interobserver variability, mainly due to individual grading practice and experience. AI-based solutions could assist pathological workflows, but their integration into clinical practice is still hampered, as they’re optimised based on general AI-metrics, rather than clinical relevance and applicability. Moreover, commercial solutions often provide similar performance than academic approaches, are expensive, and lack flexibility to adapt to new use cases. We investigate the requirements to provide a flexible AI-based histopathological tissue analysis tool, that makes the expertise of experienced pathologists accessible to every hospital in a user-friendly, open-source solution. The proposed software allows for slide inspection, tumour localisation and tissue metric extraction, while adapting to different use cases using a Python-enabled architecture. We demonstrate the value of our tool in an in-depth evaluation of transurethral hyperplastic resection tissue (TURP)-chip analysis and PCa grading using a set of extensively annotated prostate cancer patient cases. Our solution can support pathologists in challenging cases, fasten routine tasks and creates space for detail analysis.

Paper Citation

in Harvard Style

Bauer M., Schneider L., Bernhardt M., Augenstein C., Kristiansen G. and Franczyk B. (2024). An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 729-738. DOI: 10.5220/0012681900003690

in Bibtex Style

@conference{iceis24,
author={Markus Bauer and Lennart Schneider and Marit Bernhardt and Christoph Augenstein and Glen Kristiansen and Bogdan Franczyk},
title={An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={729-738},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012681900003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - An Open-Source Approach for Digital Prostate Cancer Histopathology: Bringing AI into Practice
SN - 978-989-758-692-7
AU - Bauer M.
AU - Schneider L.
AU - Bernhardt M.
AU - Augenstein C.
AU - Kristiansen G.
AU - Franczyk B.
PY - 2024
SP - 729
EP - 738
DO - 10.5220/0012681900003690
PB - SciTePress