Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

ABSTRACT: Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weigh

SUBMITTER: Chung M 

PROVIDER: S-EPMC9974793 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature