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

Publication ,  Journal Article
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 ...
Published in: Clin Cancer Res
January 17, 2023

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.

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Published In

Clin Cancer Res

DOI

EISSN

1557-3265

Publication Date

January 17, 2023

Volume

29

Issue

2

Start / End Page

364 / 378

Location

United States

Related Subject Headings

  • Young Adult
  • Rhabdomyosarcoma, Alveolar
  • Rhabdomyosarcoma
  • Prospective Studies
  • Paired Box Transcription Factors
  • Oncology & Carcinogenesis
  • Humans
  • Hematoxylin
  • Eosine Yellowish-(YS)
  • Deep Learning
 

Citation

APA
Chicago
ICMJE
MLA
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Milewski, D., Jung, H., Brown, G. T., Liu, Y., Somerville, B., Lisle, C., … Khan, J. (2023). Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group. Clin Cancer Res, 29(2), 364–378. https://doi.org/10.1158/1078-0432.CCR-22-1663
Milewski, David, Hyun Jung, G Thomas Brown, Yanling Liu, Ben Somerville, Curtis Lisle, Marc Ladanyi, et al. “Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.Clin Cancer Res 29, no. 2 (January 17, 2023): 364–78. https://doi.org/10.1158/1078-0432.CCR-22-1663.
Milewski D, Jung H, Brown GT, Liu Y, Somerville B, Lisle C, et al. Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group. Clin Cancer Res. 2023 Jan 17;29(2):364–78.
Milewski, David, et al. “Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.Clin Cancer Res, vol. 29, no. 2, Jan. 2023, pp. 364–78. Pubmed, doi:10.1158/1078-0432.CCR-22-1663.
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. Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group. Clin Cancer Res. 2023 Jan 17;29(2):364–378.

Published In

Clin Cancer Res

DOI

EISSN

1557-3265

Publication Date

January 17, 2023

Volume

29

Issue

2

Start / End Page

364 / 378

Location

United States

Related Subject Headings

  • Young Adult
  • Rhabdomyosarcoma, Alveolar
  • Rhabdomyosarcoma
  • Prospective Studies
  • Paired Box Transcription Factors
  • Oncology & Carcinogenesis
  • Humans
  • Hematoxylin
  • Eosine Yellowish-(YS)
  • Deep Learning