<b>Summary</b> 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. <b> </b> <b>Data access</b> 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 (email@example.com)<sup> </sup>and Yinyin Yuan (firstname.lastname@example.org). 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. <b>Name of Institutional Review Board or ethics committee that approved the study</b> The study was approved by the institutional review board of Duke with a waiver of the requirement to obtain informed consent.
|Date made available||2020|