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Summary

Excerpt

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 resourceBreast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. The Post-NAT-BRCA dataset is a collection of representative sections from breast resections in patients with residual invasive BC following NAT. Histologic sections were prepared and digitized to produce high resolution, microscopic images of treated BC tumors. Also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. The Residual Cancer Burden Index (RCBi), is a clinically validated tool for assessment of response to NAT associated with prognosis. Tumor cellularity is one of the parameters used for calculating the RCBi. In this dataset, tumor cellularity refers to a measure of residual disease after NAT, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (See MD Anderson RCB Calculator for a detailed description of tumor cellularity.) Malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms.

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

Localtab Group

Localtab

active	true
title	Data Access

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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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 ratersClick the Versions tab for more info about data releases.

Localtab

title	Detailed Description

Detailed Description

Number of Series

Image Statistics
Modalities	Pathology
Number of Patients	64	Number of Studies
Number of Images	96
Images Size (GB)	30

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

Localtab

title	Citations & Data Usage Policy

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:

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title	Data Citation

DOI goes here. Create using pubhub with information from Collection Approval form

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title	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.

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title	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.

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Localtab

title	Versions

Version 1 (Current): Updated 2019/

04

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26

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