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AJR Am J Roentgenol. 2016 Feb;206(2):259-64. doi: 10.2214/AJR.15.15396. Epub 2015 Nov 20.

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

Author information

1
1 Department of Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl, Box 1234, New York, NY 10029.
2
2 Department of Genetics and Genomic Science and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY.
3
3 MongoDB, New York, NY.
4
4 Department of Radiology, Clinical Informatics, Radiology IT, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY.

Abstract

OBJECTIVE:

The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging.

CONCLUSION:

The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

KEYWORDS:

breast cancer; breast imaging; data mining; precision medicine; structured reporting

PMID:
26587797
PMCID:
PMC4876713
DOI:
10.2214/AJR.15.15396
[Indexed for MEDLINE]
Free PMC Article

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