The National Research Council has defined Precision Medicine as ?the tailoring of medical treatments to individual characteristics of each patient.? This requires the ability to classify patients into specialized cohorts that differ in their susceptibility to a particular disease, in the biology and/or prognosis of the diseases they may develop, or in their response to a specific treatment. Identifying quantitative imaging phenotypes across scale through the use of radiomic/pathomic analyses is an evolving approach to cohort identification and to improving our understanding of cancer biology. These analytic techniques require large collections of well-curated data for algorithm testing and validation. Additional big data collections are required to test new hypotheses relating to cancer biology, prognosis and therapy response. Since 2011 the Cancer Imaging Archive (TCIA) has encouraged and supported cancer-related open science research by acquiring, curating, hosting and managing collections of multi-modal information. To remain relevant to its current research community and ready to support future research initiatives TCIA must undergo continuous improvement and expansion of it capabilities guided by the research community. The TCIA user community has identified four critical areas for improvement: expanded resources for integrative Image-Omics studies, enhanced capacity to acquire high quality data collections, resources to support validation studies and Research Reproducibility, and increased community engagement. The sustainment of TCIA and research community directed expansion of its capabilities will ensure this valuable resource continues to support its rapidly growing user community and continue to promote research reproducibility and data reuse in cancer precision medical research.

Public Health Relevance

Precision Medicine requires an understanding of individual variability, which can only be acquired from large data collections. Since it's inception in 2011 the Cancer Imaging Archive (TCIA) has been NCI's primary resource for managing and distributing images and related data to support Cancer Research. This project will sustain TCIA operations, expand its collections and extend the types of data it can support. This in turn will enable TCIA to play a key role in Precision Medicine research by collecting and disseminating high quality, state of the art, quantitative imaging data that meet the evolving needs of the cancer research community.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZCA1)
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Zhang, Yantian
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University of Arkansas for Medical Sciences
Schools of Medicine
Little Rock
United States
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Causey, Jason L; Zhang, Junyu; Ma, Shiqian et al. (2018) Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 8:9286
Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7
Cooper, Lee Ad; Demicco, Elizabeth G; Saltz, Joel H et al. (2018) PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective. J Pathol 244:512-524
Pantanowitz, Liron; Sharma, Ashish; Carter, Alexis B et al. (2018) Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform 9:40
Barreiros Jr, Willian; Teodoro, George; Kurc, Tahsin et al. (2017) Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems. Proc IEEE Int Conf Clust Comput 2017:25-35
Saltz, Joel; Sharma, Ashish; Iyer, Ganesh et al. (2017) A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Res 77:e79-e82