{"id":"doi:10.1016/j.jpi.2026.100715","kind":"article","title":"HistoEncoder: A digital pathology foundation model for prostate cancer","authors":["Pohjonen J","Batouche O","Kantola J","Rannikko A","Sandeman K","Erickson A","Pitkänen E","Mirtti T"],"author_count":8,"year":2026,"published":"2026-08-31","venue":"Journal of pathology informatics","cited_by":0,"doi":"10.1016/j.jpi.2026.100715","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jpi.2026.100715","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jpi.2026.100715","updated":null,"pmid":"42859633","pmcid":null,"openalex_id":null,"pdf_url":null,"abstract":"Foundation models are trained on massive amounts of data to capture complex patterns in images. Subsequently, a wide range of downstream tasks can be adopted with minimal computational resources. We have developed HistoEncoder, a foundation model for prostate cancer digital pathology by pre-training on 48 million prostate tissue tile images. HistoEncoder allows automated extraction of histological features highly predictive of Gleason patterns achieving comparable performance with substantially larger pan-cancer foundation models while being much more efficient. By fine-tuning the model with a small amount of data and computational resources, we describe two clinical use cases for HistoEncoder. First, HistoEncoder can be used to automatically annotate large-scale datasets with high accuracy. Second, we show that HistoEncoder-derived histology clusters contain prognostic information of a similar magnitude to Gleason grading in internal cross-validation. Lightweight foundation models such as HistoEncoder allow organizations to build effective clinical software tools without the need for extensive datasets and heavy computing.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"HistoEncoder: A digital pathology foundation model for prostate cancer\" by Pohjonen J, Batouche O, Kantola J et al., https://doi.org/10.1016/j.jpi.2026.100715, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-medicine","name":"AI in Clinical Medicine","domain_slug":"medicine"}],"cited_in_dossiers":[]}