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-Reference-Cited by-同舟云学术

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

Published:2023-02 Issue:2 Volume:5 Page:e71-e82
ISSN:2589-7500
Container-title:The Lancet Digital Health
language:en
Short-container-title:The Lancet Digital Health

Author:

Fremond Sarah,Andani Sonali,Barkey Wolf Jurriaan,Dijkstra Jouke,Melsbach Sinéad,Jobsen Jan J,Brinkhuis Mariel,Roothaan Suzan,Jurgenliemk-Schulz Ina,Lutgens Ludy C H W,Nout Remi A,van der Steen-Banasik Elzbieta M,de Boer Stephanie M,Powell Melanie E,Singh Naveena,Mileshkin Linda R,Mackay Helen J,Leary Alexandra,Nijman Hans W,Smit Vincent T H B M,Creutzberg Carien L,Horeweg Nanda,Koelzer Viktor H,Bosse Tjalling

Publisher

Elsevier BV

Subject

Health Information Management,Decision Sciences (miscellaneous),Health Informatics,Medicine (miscellaneous)

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4. Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy;León-Castillo;J Clin Oncol,2020

5. Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative;Stelloo;Mod Pathol,2015

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3. Translating biological insights into improved management of endometrial cancer;Nature Reviews Clinical Oncology;2024-08-28

4. MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images;Scientific Reports;2024-08-06

5. A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles;PLOS ONE;2024-07-31