The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research

• Type of Material: Article
• Authors: David Andrew Gutman, Emory University; Mohammed Khalilia, Emory University; Sanghoon Lee, Emory University; Michael Nalisnik, Emory University; Zach Mullen, Kitware Incorporated; Jonathan Beezley, Kitware Incorporated; Deepak R. Chittajallu, Kitware Incorporated; David Manthey, Kitware Incorporated; Lee Cooper, Emory University
• Language: English
• Date: 2017-11-01
• Publisher: American Association for Cancer Research
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 American Association for Cancer Research.
• Final Published Version (URL): http://dx.doi.org/10.1158/0008-5472.CAN-17-0629
• Title of Journal or Parent Work: Cancer Research
• ISSN: 0008-5472
• Volume: 77
• Issue: 21
• Start Page: E75
• End Page: E78
• Grant/Funding Information: Funding statement: this work is supported by the National Cancer Institute Informatics Technology for Cancer Research grant number U24-CA194362-02.
• Keywords: Life Sciences & Biomedicine; Oncology; Science & Technology
• Research Categories: Engineering, Biomedical; Biology, Neuroscience

Abstract

Tissue-based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information and can be digitized into expansive and high-resolution whole-slide images using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share, and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this article, we describe the Digital Slide Archive (DSA), an open-source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities.