Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study
Year: 2026
Type: article
Abstract: Mitotic figure counting is an established measure of cell proliferation that is included in grading systems. We developed a deep learning method for mitotic figure counting and evaluated its prognostic impact in multiple external validation datasets. The deep learning method was trained in whole slide images of tissue sections stained with haematoxylin and eosin from a publicly available breast cancer dataset where mitotic figures have been annotated by expert pathologists. The final model was externally validated according to a protocol with predefined analyses of 14 571 patient samples from 13 patient cohorts from seven different cancer types. The predefined primary analysis was univariable Cox survival analysis of the number of mitotic figures detected per mm 2 . Automatic mitotic figure counting correlated well with known proliferation rates, and patients with more mitotic figures per mm 2 had significantly worse patient outcome in all the studied cancer types except colorectal cancer. This study demonstrates the practical potential of automated, deep learning‐based mitotic figure counting, both by automating pathology work and by suggesting expanded use in more cancer types, such as prostate cancer.
Source: FEBS Open Bio
Authors: Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem
Institutions: Oslo University Hospital, University of Oslo, UiT The Arctic University of Norway, Stavanger University Hospital, University of Stavanger, University of Liverpool, Liverpool University Hospitals NHS Foundation Trust, Drammen Hospital, Vestre Viken Hospital Trust, Sykehuset i Vestfold, University Hospital of North Norway, Cheltenham General Hospital, University College London, University of Oxford
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,210
Funder: Norges Forskningsråd
Awards: G5832297111, G8131153181
OpenAlex ID: W7128707347