Yearb Med Inform 2017; 26(01): 110-119
DOI: 10.15265/IY-2017-041
Section 4: Sensor, Signal and Imaging Informatics
Survey
Georg Thieme Verlag KG Stuttgart

An Assessment of Imaging Informatics for Precision Medicine in Cancer

C. Chennubhotla
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
,
L. P. Clarke
Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
A. Fedorov
Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
,
D. Foran
Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, New Brunswick, NJ, USA
,
G. Harris
Harvard Medical School, Boston, MA, USA
,
E. Helton
Center for Biomedical Informatics and Information Technology, NCI, NIH, Bethesda, MD, USA
,
R. Nordstrom
Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
F. Prior
Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
,
D. Rubin
Department of Radiology, Stanford University, Palo Alto, CA, USA
,
J. H. Saltz
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
,
E. Shalley
Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
A. Sharma
Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
11 September 2017 (online)

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Summary

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology.

Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein.

Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician’s feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so.

Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.

Keywords

Quantitative imaging - precision medicine - Big Data - information dissemination/methods - diagnostic imaging/trends