ArticleJournal of the American Medical Informatics Association : JAMIA
Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
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Who cites it
86 citing papers in PubMed.
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- Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma.bioRxiv : the preprint server for biology · 2026Article
- A pathology-to-single-cell framework links SPP1Journal of translational medicine · 2026Article
- Global mRNA 3'UTR lengthening in small-cell neuroendocrine carcinoma.bioRxiv : the preprint server for biology · 2026Article
- Review
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- Segment Anything Model for Gastric Cancer.Cancer medicine · 2025Article
- Enhancing image retrieval through optimal barcode representation.Scientific reports · 2025Article
- Investigation on potential bias factors in histopathology datasets.Scientific reports · 2025Article
- Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.Journal of translational medicine · 2025Article
- Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer.Frontiers in immunology · 2025Article
- Deep feature batch correction using ComBat for machine learning applications in computational pathology.Journal of pathology informatics · 2024Article
- A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.The American journal of pathology · 2024Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- Identification and validation of a prognostic model based on three TLS-Related genes in oral squamous cell carcinoma.Cancer cell international · 2024Article
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.NPJ precision oncology · 2024Article
- MMIR: an open-source software for the registration of multimodal histological images.BMC medical informatics and decision making · 2024Article
26 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
backgroundThe integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types.
objectiveTo build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. MATERIALS AND
methodsAll images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites.
resultsThe Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types. DISCUSSION: The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics.
conclusionsWith the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.
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