Margolies Laurie R, Pandey Gaurav, Horowitz Eliot R, Mendelson David S
1 Department of Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl, Box 1234, New York, NY 10029.
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.
AJR Am J Roentgenol. 2016 Feb;206(2):259-64. doi: 10.2214/AJR.15.15396. Epub 2015 Nov 20.
The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging.
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.
本文旨在描述结构化报告以及用于乳腺成像数据挖掘的大型数据库的开发。
数百万次乳腺成像检查的结果通过基于BI-RADS词典的结构化工具进行报告。这些数据大多存储在可访问的介质中。强大的计算能力为数据科学家和乳腺成像专家合作改善乳腺癌检测及优化筛查算法创造了巨大机会。数据挖掘可以创造知识，但所提出的问题及其复杂性需要极其强大且灵活的数据库。新的数据技术可以促进结果研究和精准医学。
