» Articles » PMID: 29092945

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

Overview
Journal Cancer Res
Specialty Oncology
Date 2017 Nov 3
PMID 29092945
Citations 76
Authors
Affiliations
Soon will be listed here.
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. .

Citing Articles

An update on applications of digital pathology: primary diagnosis; telepathology, education and research.

Zia S, Yildiz-Aktas I, Zia F, Parwani A Diagn Pathol. 2025; 20(1):17.

PMID: 39940046 PMC: 11817092. DOI: 10.1186/s13000-025-01610-9.


GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning.

Mateos-Aparicio-Ruiz I, Pedraza A, Becker J, Altini N, Salido J, Bueno G Comput Struct Biotechnol J. 2025; 27():35-47.

PMID: 39802211 PMC: 11719282. DOI: 10.1016/j.csbj.2024.11.049.


Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.

Kim H, Kim J, Yeon S, You S Front Oncol. 2024; 14:1465098.

PMID: 39678498 PMC: 11638011. DOI: 10.3389/fonc.2024.1465098.


MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath.

Kauer T, Sehring J, Schmid K, Bartkuhn M, Wiebach B, Crnkovic S J Imaging. 2024; 10(11).

PMID: 39590756 PMC: 11595786. DOI: 10.3390/jimaging10110292.


Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.

Ghandian S, Albarghouthi L, Nava K, Sharma S, Minaud L, Beckett L bioRxiv. 2024; .

PMID: 39386601 PMC: 11463656. DOI: 10.1101/2024.05.15.594372.


References
1.
Heng Y, Lester S, Tse G, Factor R, Allison K, Collins L . The molecular basis of breast cancer pathological phenotypes. J Pathol. 2016; 241(3):375-391. PMC: 5499709. DOI: 10.1002/path.4847. View

2.
Wang C, Pecot T, Zynger D, Machiraju R, Shapiro C, Huang K . Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc. 2013; 20(4):680-7. PMC: 3721170. DOI: 10.1136/amiajnl-2012-001538. View

3.
Kong J, Cooper L, Wang F, Gao J, Teodoro G, Scarpace L . Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. PLoS One. 2013; 8(11):e81049. PMC: 3827469. DOI: 10.1371/journal.pone.0081049. View

4.
Cooper L, Kong J, Gutman D, Dunn W, Nalisnik M, Brat D . Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest. 2015; 95(4):366-76. PMC: 4465352. DOI: 10.1038/labinvest.2014.153. View

5.
Singh R, Chubb L, Pantanowitz L, Parwani A . Standardization in digital pathology: Supplement 145 of the DICOM standards. J Pathol Inform. 2011; 2:23. PMC: 3097525. DOI: 10.4103/2153-3539.80719. View