Skip to main content

TIL-WSI-TCGA

TIL-WSI-TCGA | Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images

DOI: 10.7937/K9/TCIA.2018.Y75F9W1 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Supporting Data Updated
Bladder Urothelial Carcinoma, Breast Invasive Carcinoma, Cervical Squamous Cell Carcinoma, Endocervical Adenocarcinoma, Colon adenocarcinoma, Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Pancreatic adenocarcinoma, Prostate Adenocarcinoma, Rectum Adenocarcinoma, Skin Cutaneous Melanoma, Stomach Adenocarcinoma, Uterine Corpus Endometrial Carcinoma, Uveal Melanoma Bladder, Breast, Pelvic Cervix, Chest, Colon, Eye, Lung, Pancreas, Prostate, Rectum, Skin, Stomach, Uterus 4,759 Deep learning based computational stain for staining tumor-infiltrating lymphocytes (TILs), Software/Source Code 2018/12/17

Summary

Mappings of tumor-infiltrating lymphocytes (TILs), based on H&E images from 13 of The Cancer Genome Atlas (TCGA) tumor types are available here. These TIL maps are derived through computational staining, using a convolutional neural network trained to classify patches of images. In addition to the TIL Maps, the analysis codes and the software used to extract TILs are also available.  The accompanying paper contains detailed information about our methods and our findings.  The source histopathology, molecular correlates and clinical data used in this study can be found on the Genomic Data Commons.  More information about the tools used to generate these results can be found  on the QuIP Software Stack and TIL Classification Software pages.  Answers to commonly asked questions about these data are contained in this FAQs document.

TCGA Tumor Types Used in this Study

  1. BLCA Bladder urothelial carcinoma
  2. BRCA Breast invasive carcinoma
  3. CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma
  4. COAD Colon adenocarcinoma
  5. LUAD Lung adenocarcinoma
  6. LUSC Lung squamous cell carcinoma
  7. PAAD Pancreatic adenocarcinoma
  8. PRAD Prostate adenocarcinoma
  9. READ Rectum adenocarcinoma
  10. SKCM Skin Cutaneous Melanoma
  11. STAD Stomach adenocarcinoma
  12. UCEC Uterine Corpus Endometrial Carcinoma
  13. UVM Uveal Melanoma

Data Access

Version 1: Updated 2018/12/17

Title Data Type Format Access Points Subjects Studies Series Images License
Histopathology TIL Maps CC BY 3.0

Additional Resources For This Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

Collections Used in this Third Party Analysis

Below is a list of TCIA Collections used in these analyses:

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., & Thorsson, V. (2018). Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1

Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a publication you’d like to add please contact TCIA’s Helpdesk.

Publication Citation

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., Thorsson, V. (2018). Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23(1), 181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Collections Used In This Analysis Result

Related Collections