Metadata record for the manuscript: Unmasking the immune microecology of ductal carcinoma in situ with deep learning

Summary

This metadata record provides details of the
data supporting the claims of the related manuscript: “Unmasking the immune microecology
of ductal carcinoma in situ with deep learning”.

The data consist of immunohistochemistry (IHC)
haematoxylin and eosin (H&E) staining images of grade 2-3 pure ductal
carcinoma in situ (DCIS) and DCIS adjacent to invasive cancer (adjacent DCIS)
samples.

The related study aimed to characterise tissue
spatial architecture and the microenvironment of DCIS via design and validation
of a new deep learning pipeline.

Data
access

All training data, including the fully
anonymised raw H&E image tiles and pathological annotations as binary
marks, as well as Python code, are available in the corresponding author’s
GitHub: https://github.com/pathdata/HE_Tissue_Segmentation.
Requests for data access for the Duke samples can be submitted to E. Shelley
Hwang ([e-mail]) and Yinyin Yuan ([e-mail]).
Data underlying Figures 4 and 6 are in the files “Ext_validData_DCIS_DAVE_Fig4_data.csv”
and “Ext_validData_DCIS_DAVE_Fig6_data.csv”, included with this metadata
record. The images used as representative examples in Figure 8 are listed in
the file “Figure 8 image details.xlsx”, included with this metadata record.

Name
of Institutional Review Board or ethics committee that approved the study

The study was approved by the institutional
review board of Duke with a waiver of the requirement to obtain informed
consent.