Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

ABSTRACT:

Background

Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening.

Methods

We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick

SUBMITTER: Lehman CD 

PROVIDER: S-EPMC9552206 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature