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  • Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology (Post-NAT-BRCA)

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Summary


To validate different methods for computing TC(?), representative sections from 64 patients with residual invasive breast cancer on resection specimens following neoadjuvant therapy(NAT) were acquired. Representative routine Hematoxylin and Eosin glass slides were scanned at 20X magnification (0.5 lm/pixel) using an Aperio scanner. The study was approved by the institutional ethics board and an amendment to share the data was obtained. Clinical data corresponding to each patient includes: Age at diagnosis, Menopausal status, NAT type (chemo/HER2/RAD/Endocrine), Histological type, ER/PR/HER2 status, LN(?) status. A breast pathology fellow annotated identified patches on a digital pathology viewing platform, Sedeen Viewer. For each patch, a TC score, ranging from 0% to 100% for assessment of RCB(?), was provided. Patches which did not contain any tumour cells were assigned a TC score of 0%. Annotations for >30,000 nuclei (3,868 lymphocyte, 10,407 benign epithelial, and 16,419 malignant epithelial figures) were also marked by the pathologist from 166 ROIs in sets A and B which included a mixture of lymphocyte, benign, and malignant epithelial nuclei figures.

This data was used for the BreastPathQ challenge co-sponsored by SPIE/AAPM/NCI and currently 338 participants have registered on the CodaLab site to download patches from these WSIs. The aim of the data collection was to train and validate methods of assessing residual tumour cellularity after NAT. We also have annotations for malignant and benign epithelial cells and TILs which are valuable for assessing performance of cell classification methods. This collection is distinct from other collections as it is focused on post-NAT patients with residual tumour and was collected to allow automatic methods of residual cancer burden to be developed. There are many histopathological changes that occur after therapy and the image data is linked with information about receptor status and treatment type making this a unique resource.


Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • Funding from the Canadian Cancer Society, grant #705772 National Cancer institute of the National Institutes of Health under #U24CA199374

Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

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Supplemental Data (XML)

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Detailed Description

Image Statistics


Modalities

Pathology

Number of Patients

64

Number of Studies


Number of Series


Number of Images

96

Images Size (GB)

Citations & Data Usage Policy

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These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

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Publication Citation

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.

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. 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.

Version 1 (Current): Updated 2019/04/26

Data TypeDownload all or Query/Filter
Images (SVS)

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Clinical Data (CSV)Link
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