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
| Location | Subjects | Size | Updated | |||
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| 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, Histopathology | 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
Data Access
Version 1: Updated 2018/12/17
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Histopathology TIL Map Browser | Histopathology | CSV | 4,759 | 5,202 | 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.
- Genomic Data Commons: (Genomic data, H&E Images , Clinical, and molecular correlate 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.
- Software: QuIP Software Stack
- Software: TIL Classification Software
- TIL Maps
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 |
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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 |
Related Publications
Publication Citation |
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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 |
Research Community Publications
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