Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.

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Title: Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.
Authors: Fremond S; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands., Andani S; Department of Computer Science, ETH Zurich, Zurich, Switzerland; Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland., Barkey Wolf J; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands., Dijkstra J; Department of Vascular and Molecular Imaging, Leiden University Medical Center, Leiden, Netherlands., Melsbach S; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands., Jobsen JJ; Department of Radiation Oncology, Medisch Spectrum Twente, Enschede, Netherlands., Brinkhuis M; Department of Pathology, LabPON, Hengelo, Netherlands., Roothaan S; Department of Pathology, LabPON, Hengelo, Netherlands., Jurgenliemk-Schulz I; Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands., Lutgens LCHW; Department of Radiation Oncology, Maastricht University Medical Center+, Maastricht, Netherlands., Nout RA; Department of Radiation Oncology, Erasmus University Medical Center, Rotterdam, Netherlands., van der Steen-Banasik EM; Department of Radiation Oncology, Radiotherapiegroep, Arnhem, Netherlands., de Boer SM; Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands., Powell ME; Department of Clinical Oncology, Barts Health NHS Trust, London, UK., Singh N; Department of Pathology, Barts Health NHS Trust, London, UK., Mileshkin LR; Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, VIC, Australia., Mackay HJ; Department of Medical Oncology and Hematology, Odette Cancer Center Sunnybrook Health Sciences Center, Toronto, ON, Canada., Leary A; Medical Oncology Department, Gustave Roussy Institute, Villejuif, France., Nijman HW; Department of Obstetrics and Gynecology, University Medical Center Groningen, Groningen, Netherlands., Smit VTHBM; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands., Creutzberg CL; Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands., Horeweg N; Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands., Koelzer VH; Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. Electronic address: viktor.koelzer@usz.ch., Bosse T; Department of Pathology, Leiden University Medical Center, Leiden, Netherlands. Electronic address: t.bosse@lumc.nl.
Source: The Lancet. Digital health [Lancet Digit Health] 2023 Feb; Vol. 5 (2), pp. e71-e82. Date of Electronic Publication: 2022 Dec 07.
Publication Type: Randomized Controlled Trial; Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101751302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2589-7500 (Electronic) Linking ISSN: 25897500 NLM ISO Abbreviation: Lancet Digit Health Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2589-7500
DOI:10.1016/S2589-7500(22)00210-2