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 Hall, Corinne M. Linardic, Jun S. Wei, Hsien Chao Chou, Stephen X. Skapek, Rajkumar Venkatramani, Peter K. Bode, Seth M. Steinberg, George ZakiIgor B. Kuznetsov, Douglas S. Hawkins, Jack F. Shern, Jack Collins, Javed Khan

Research output: Contribution to journalArticlepeer-review


PURPOSE: Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS. EXPERIMENTAL DESIGN: Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn features associated with mutations and relative event-free survival risk. The performance of the trained models was evaluated against independent test sample data (n = 136) or holdout test data. RESULTS: The trained CNN could accurately classify alveolar RMS, a high-risk subtype associated with PAX3/7-FOXO1 fusion genes, with an ROC of 0.85 on an independent test dataset. CNN models trained on mutationally-annotated samples identified tumors with RAS pathway with a ROC of 0.67, and high-risk mutations in MYOD1 or TP53 with a ROC of 0.97 and 0.63, respectively. Remarkably, CNN models were superior in predicting event-free and overall survival compared with current molecular-clinical risk stratification. CONCLUSIONS: 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.

Original languageEnglish (US)
Pages (from-to)364-378
Number of pages15
JournalClinical cancer research : an official journal of the American Association for Cancer Research
Issue number2
StatePublished - Jan 17 2023

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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