Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results.

Purpose: Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characterization of normal and pathological lesion tissue to assist radiologists in defining different diseases. We introduce a novel tissue signature model based on tissue characteristics in breast tissue from multiparametric magnetic resonance imaging (mpMRI).

Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging.

Purpose: To evaluate the incremental value of diffusion-weighted (DW) imaging and apparent diffusion coefficient (ADC) mapping in relation to conventional breast magnetic resonance (MR) imaging in the characterization of benign versus malignant breast lesions at 3.0 T.

Materials And Methods: This retrospective HIPAA-compliant study was approved by the institutional review board, with the requirement for informed patient consent waived.