ExpressScan: histological whole-slide image data from the Athero-Express (AE) and Aneurysm-Express (AAA) Biobank Studies

DOI

Background The ExpressScan is an ongoing, unfunded project to scan pathological slides of atherosclerotic plaques and aneurysm tissues at high-resolution using pathology scanners into whole-slide images (WSI). Here we describe these histological WSI data available for the Athero-Express (AE) and Aneurysm-Express (AAA) Biobank Studies.

The AE started in 2002 and now includes over 3,500 patients who underwent surgery to remove atherosclerotic plaques (endarterectomy) from one (or more) of their major arteries (majority carotids and femorals). The AAA started in 2003 and now includes over 1,000 patients who underwent open surgery on arterial aneurysms, the majority on aortic aneurysms. The staining protocols are described by Verhoeven et al. (AE) and Hurks et al. (AAA).

Whole-slide images are available for the several commonly used stains, but note that these are not available in all samples in both studies. A table showing the approximate numbers of available WSI is given here.

slideToolKit These data are also used for slideToolKit and other ExpressScan projects (see below). Depending on the content of the project, a list of slides used is available to enable reproducible science.

Associated ExpressScan projects - Glycophorin C: Mekke JM et al. and the associated GitHub repository. - slideEMask: entropy based tissue masker - slideNormalize: normalize WSIhttps://github.com/swvanderlaan/slideNormalize - ExpressScan-Unlock: local, on-campus webportal to inspect WSI (private, ongoing and unpublished) - ExpressScan_QC: quality control procedures to process results from slideToolKit (private, ongoing and unpublished) - EntropyMasker: improved method to automatically mask WSI using entropy (private, ongoing and unpublished) - CONVOCALS: a deep-learning classification project on plaques (private, ongoing and unpublished) - DEEP-ENIGMA: a deep-learning image-segmentation project on plaques (private, ongoing and unpublished)

A link to the public GitHub repository for slideToolKit can be found here: https://github.com/swvanderlaan/slideToolKit.

Important notice on availability of data The amount of data is huge: over 25,000 WSI on average 1Gb size per WSI. There are also restrictions on use by commercial parties, and on sharing openly based on (inter)national laws and regulations and the written informed consent. Therefore these data (and additional clinical data) are only available upon discussion and signing a Data Sharing Agreement (see Terms of Access) and within a specially designed UMC Utrecht provided environment.

Identifier
DOI https://doi.org/10.34894/QI135J
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/QI135J
Provenance
Creator Sander W. van der Laan ORCID logo; Gerard Pasterkamp; Gert Jan de Borst; Dominique P.V. de Kleijn ORCID logo; Joost A. van Herwaarden ORCID logo
Publisher DataverseNL
Contributor dLAB Data Management; Sander W. van der Laan
Publication Year 2022
Rights info:eu-repo/semantics/closedAccess
OpenAccess false
Contact dLAB Data Management (UMC Utrecht); Sander W. van der Laan (UMC Utrecht)
Representation
Resource Type Dataset
Format application/pdf; application/msword; text/plain
Size 61663; 64158; 34816; 2904
Version 2.0
Discipline Life Sciences; Medicine