Rhabdomyosarcoma Images Now Available Through NCI’s Imaging Data Commons

You can now access digital, whole-slide, hematoxylin and eosin (H&E) stained Rhabdomyosarcoma (RMS) images in DICOM format1 via NCI’s Imaging Data Commons (IDC)2.

RMS is a highly aggressive soft tissue sarcoma that predominantly affects children and young adults. Past research has identified specific genomic alterations in RMS strongly associated with survival. However, predicting these mutations or identifying high-risk disease at the time of diagnosis remains a significant challenge.

The images are from diagnostic specimens of 403 RMS patients who participated in Children’s Oncology Group trials between 1998 and 20173. You have access to metadata added by researchers and the benefit of DICOM whole-slide microscopy representation4,5 using custom, open-source scripts and tools6.

In addition, you can get:

Researchers used these diagnostic H&E images to train deep learning models for identifying tumor features, predicting the presence of high-risk mutations, assessing relative risk, and predicting molecular subtypes and survival in RMS8. This methodology extends to other pediatric and adult malignancies, with the potential to integrate deep learning algorithms into clinical decision-making and precision medicine. Such advancements hold promise for the future of cancer care.


1 DICOM Converted Whole Slide Hematoxylin and Eosin Images of Rhabdomyosarcoma from Children's Oncology Group Trials. Zenodo, 2023.

2 NCI Imaging Data Commons. Cancer Research, 2021.

3 Genomic Classification and Clinical Outcome in Rhabdomyosarcoma: A Report from an International Consortium. Journal of Clinical Oncology, 2021.

4 National Electrical Manufacturers Association (NEMA). DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD. Accessed September 5, 2023.

5 Implementing the DICOM Standard for Digital Pathology. Journal of Pathology Informatics, 2018.

6 IDC-WSI-Conversion: RMS. Accessed September 5, 2023.

7 IDC-Tutorials: IDC Clinical Data Exploration. Accessed September 5, 2023.

8 Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children’s Oncology Group. Clinical Cancer Research, 2023.

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