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Open AccessJournal ArticleDOI

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

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TLDR
The management tasks and user support model for TCIA is described, an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer.
Abstract
The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)—an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

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Journal ArticleDOI

Radiomics: Images Are More than Pictures, They Are Data.

TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Journal ArticleDOI

The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.

TL;DR: The current status of TCGA Research Network structure, purpose, and achievements are discussed, to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer.
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Convolutional neural networks: an overview and application in radiology

TL;DR: A perspective on the basic concepts of convolutional neural network and its application to various radiological tasks is offered, and its challenges and future directions in the field of radiology are discussed.
Journal ArticleDOI

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

TL;DR: This set of labels and features should enable direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as performance evaluation of computer-aided segmentation methods.
Journal ArticleDOI

Artificial intelligence in radiology

TL;DR: A general understanding of AI methods, particularly those pertaining to image-based tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology.
References
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Journal ArticleDOI

Cancer Genome Atlas

Tracy Hampton
- 25 Oct 2006 -