The Screening Tomosynthesis Trial with Advanced Reader Methods (STREAM): design and rationale of a population-based breast cancer screening trial.

Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.

Materials And Methods: The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50-72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT.

Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.

Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.

Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.