Data Access
Data Type |
Download all or Query/Filter |
Images (SVS, 43.2 GB) |
|
Annotations (XLSX) |
|
Please note that Box has a 15GB download limit, so you will need to download images in batches.
|
Detailed Description
|
|
Modalities
|
Pathology
|
Number of Participants
|
64
|
Number of Images
|
96
|
Images Size (GB) |
43.2
|
Content
The Post-NAT-BRCA dataset is composed of:
• 96 whole slide images stored in an uncompressed .svs file format, standard for pathology slides. Slides were scanned at 20x objective on an Aperio slide scanner at Sunnybrook Health Sciences Centre.
• An Excel (.xlsx) file containing clinical features for each patient including age, treatment, ER/PR/HER2 status etc. A key and detailed description of each column is provided in a separate tab titled "Definitions". Each row in the spreadsheet corresponds to a single (.svs) slide and anonymised patient ID's are provided in a separate column.
• Manual annotations of tumor cellularity and cell labels, provided as Sedeen annotation files (.xml). Annotations are given in two directories, where the "WSI_train" folder contains WSIs annotated by a single rater and "WSI_test" was annotated by two raters.
Recommended Software
To browse whole slide images and annotations, we highly recommend you use Pathcore's Sedeen Viewer which is available for free: https://pathcore.com/sedeen/
Please ensure that the "sedeen" folder is unzipped and placed into the same folder containing the .svs files. Sedeen Viewer loads annotations from this folder automatically when images are opened.
Upon opening WSIs in Sedeen, you will notice that annotations have been color-coded according to the following key:
- Pink: Healthy (0% tumor cellularity)
- Blue: Low tumor cellularity (0 - 30%)
- Yellow: Medium tumor cellularity (31 - 70%)
- Green: High tumor cellularity (70 - 100%)
- White: Contains annotations at the cell level and labeled as:
- Lymphocyte: TIL-E, TIL-S
- Normal Epithelial: normal, UDH, ADH,
- Malignant Epithelial: IDC, ILC, Muc C, DCIS 1, DCIS 2, DCIS 3, MC- E, MC - C, MC - M
|
Citations & Data Usage Policy
Add any special restrictions in here.
"Martel, A. L., Nofech-Mozes, S., Salama, S., Akbar, S., & Peikari, M. (2019). Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.4YIBTJNO"
|
1) Peikari, M., Salama, S., Nofech-Mozes, S. and Martel, A.L., 2017. Automatic cellularity assessment from post-treated breast surgical specimens. Cytometry Part A, 91(11), pp.1078-1087.
|
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
|
Other Publications Using This Data
TCIA maintains
a list of publications
which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
- Akbar, S., Peikari, M., Salama, S., Panah, A. Y., Nofech-Mozes, S., Martel, A. L. (2019) Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment. Sci Rep, 9, 14099. https://doi.org/10.1038/s41598-019-50568-4
|
Version 1 (Current): Updated 2019/10/01
Data Type
|
Download all or Query/Filter
|
Images (SVS, 43.2 GB)
|
|
Annotations (XLSX
)
|
|
Please note that Box has a 15GB download limit, so you will need to download images in batches.
|
|