A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.
Journal:
JCO clinical cancer informatics
Published Date:
May 1, 2019
Abstract
PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk.
Authors
Keywords
Algorithms
Area Under Curve
Biopsy
Breast Neoplasms
Databases, Factual
Decision Support Systems, Clinical
Deep Learning
Electronic Health Records
Expert Systems
Female
Humans
Image Processing, Computer-Assisted
Mammography
Medical Informatics
Precision Medicine
Reproducibility of Results
Risk Assessment