Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients using Deep Learning of H&E Images: A Report from the Children's Oncology Group.

David Milewski Hyun Jung G Thomas Brown Yanling Liu Ben Somerville Curtis Lisle Marc Ladanyi Erin R Rudzinski Hyoyoung Choo-Wosoba Donald A Barkauskas Tammy Lo David C Hall Corinne M Linardic Jun S Wei Hsien-Chao Chou Stephen X Skapek Rajkumar Venkatramani Peter Karl Bode Seth M Steinberg George Zaki Igor B Kuznetsov Douglas S Hawkins Jack F Shern Jack Collins Javed Khan

Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2022)

This study demonstrates that high-risk features, including those associated with certain mutations, can be readily identified at diagnosis using deep learning. CNNs are a powerful tool for diagnostic and prognostic prediction of rhabdomyosarcoma which will be tested in prospective COG clinical trials.

Keyphrases

deep learning
clinical trial
convolutional neural network
end stage renal disease
artificial intelligence
chronic kidney disease
ejection fraction
newly diagnosed
machine learning
prognostic factors
patient reported outcomes
single molecule
study protocol