Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer

Abstract

The aggressiveness of prostate cancer is primarily assessed from histopathological data using the Gleason scoring system. Conventional artificial intelligence (AI) approaches can predict Gleason scores, but often lack explainability, which may limit clinical acceptance. Here, we present an alternative, inherently explainable AI that circumvents the need for post-hoc explainability methods. The model was trained on 1,015 tissue microarray core images, annotated with detailed pattern descriptions by 54 international pathologists following standardized guidelines. It uses pathologist-defined terminology and was trained using soft labels to capture data uncertainty. This approach enables robust Gleason pattern segmentation despite high interobserver variability. The model achieved comparable or superior performance to direct Gleason pattern segmentation (Dice score: 0:713 ± 0:003 vs. 0:691 ± 0:010) while providing interpretable outputs. We release this dataset to encourage further research on segmentation in medical tasks with high subjectivity and to deepen insights into pathologists’ reasoning.

Metadata

Item Type:	Article
Authors/Creators:	Mittmann, G. https://orcid.org/0009-0005-0375-4689 Laiouar-Pedari, S. Mehrtens, H.A. Haggenmüller, S. Bucher, T.-C. Chanda, T. https://orcid.org/0009-0009-0649-9301 Gaisa, N.T. Wagner, M. Klamminger, G.G. Rau, T.T. Neppl, C. Compérat, E.M. Gocht, A. Haemmerle, M. https://orcid.org/0000-0003-3145-0541 Rupp, N.J. Westhoff, J. Krücken, I. Seidl, M. https://orcid.org/0000-0002-5559-4980 Schürch, C.M. https://orcid.org/0000-0002-1792-1768 Bauer, M. https://orcid.org/0000-0002-0657-6831 Solass, W. Tam, Y.C. Weber, F. https://orcid.org/0000-0001-7480-8828 Grobholz, R. https://orcid.org/0000-0002-1901-7967 Augustyniak, J. Kalinski, T. Hörner, C. Mertz, K.D. Döring, C. Erbersdobler, A. Deubler, G. Bremmer, F. Sommer, U. Brodhun, M. Griffin, J. https://orcid.org/0000-0003-3969-7637 Lenon, M.S.L. Trpkov, K. Cheng, L. https://orcid.org/0000-0001-6049-5293 Chen, F. https://orcid.org/0000-0001-8005-4399 Levi, A. Cai, G. Nguyen, T.Q. Amin, A. Cimadamore, A. Shabaik, A. Manucha, V. Ahmad, N. Messias, N. Sanguedolce, F. Taheri, D. Baraban, E. Jia, L. Shah, R.B. Siadat, F. Swarbrick, N. Park, K. Hassan, O. Sakhaie, S. Downes, M.R. Miyamoto, H. https://orcid.org/0000-0001-7610-7769 Williamson, S.R. Holland-Letz, T. Wies, C. https://orcid.org/0000-0001-7136-298X Schneider, C.V. Kather, J.N. Tolkach, Y. https://orcid.org/0000-0001-5239-2841 Brinker, T.J. https://orcid.org/0000-0002-3620-5919
Copyright, Publisher and Additional Information:	© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Dates:	Submitted: 3 March 2025 Accepted: 22 September 2025 Published (online): 8 October 2025 Published: 8 October 2025
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Date Deposited:	09 Oct 2025 07:34
Last Modified:	09 Oct 2025 07:34
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s41467-025-64712-4
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:232737

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Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer

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