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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Milewski, D; Jung, H; Brown, GT; Liu, Y; Somerville, B; Lisle, C; Ladanyi, M; Rudzinski, ER; Choo-Wosoba, H; Barkauskas, DA; Lo, T; Hall, D; Linardic, CM; Wei, JS; Chou, H-C; Skapek, SX; Venkatramani, R; Bode, PK; Steinberg, SM; Zaki, G; Kuznetsov, IB; Hawkins, DS; Shern, JF; Collins, J; Khan, J

Published Date

  • January 17, 2023

Published In

Volume / Issue

  • 29 / 2

Start / End Page

  • 364 - 378

PubMed ID

  • 36346688

Pubmed Central ID

  • PMC9843436

Electronic International Standard Serial Number (EISSN)

  • 1557-3265

Digital Object Identifier (DOI)

  • 10.1158/1078-0432.CCR-22-1663


  • eng

Conference Location

  • United States