DOI: 10.5281/zenodo.13346196
title: DICOM converted Slide Microscopy images for the TCGA-TGCT collection

description: This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute
Imaging Data Commons (IDC) [1]. This dataset was converted
into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using
IDC Portal here: TCGA-TGCT. You can use the manifests included in this
Zenodo record to download the content of the collection following the Download instructions below.

Collection description


      More than 90% of testicular cancer start in the germ cells, which are cells in the testicles and develop into sperm. This type of cancer is known as testicular germ cell cancer. Testicular germ cell cancer can be classified as either seminomas or nonseminomas, which may be identified by microscopy. Nonseminomas typically grow and spread more quickly than seminomas. A testicular germ cell tumor that contains a mix of both these subtypes is classified as a nonseminoma. TCGA studied both seminomas and nonseminomas.



      Testicular germ cell cancer is rare, comprising 1-2% of all tumors in males. However, it is the most common cancer in men ages 15 to 35. The incidence of testicular germ cell cancer has been continuously rising in many countries, including Europe and the U.S. In 2013, about 8,000 American men were estimated to be diagnosed with the cancer. Of those, 370 are predicted to die from the disease. Men who are Caucasian, have an undescended testicle, abnormally developed testicles, or a family history of testicular cancer have a greater risk of developing testicular cancer. Fortunately, testicular germ cell cancer is highly treatable.


       Please see the TCGA-TGCT information page to learn more about the images and to obtain any supporting metadata for this collection.


      Citation guidelines can be found on the Citing TCGA in Publications and Presentations information page.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced.
For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the
collection_id collection introduced in IDC data
release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of
the corresponding collection was introduced.



tcga_tgct-idc_v10-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets

tcga_tgct-idc_v10-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets

tcga_tgct-idc_v10-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)


Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference
files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.


Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:



install idc-index package: pip install --upgrade idc-index

download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.


To download the files using .dcf manifest, see manifest header.


Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute,
National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.


References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann,
M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer,
D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R.
National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in
Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180