AI-based prevention of interval cancers in a national mammography screening program.

Purpose: To demonstrate that artificial intelligence (AI) can detect and correctly localise retrospectively visible cancers that were missed and diagnosed as interval cancers (false negative (FN) and minimal signs (MS) interval cancers), and to characterise AI performance on non-visible occult and true interval cancers.

Method: Prior screening mammograms from N = 2,396 women diagnosed with interval breast cancer between March 2006 and May 2018 in north-western Germany were analysed with an AI system, producing a model score for all studies. All included studies previously underwent independent radiological review at a mammography reference centre to confirm interval cancer classification.

[Systematic and quality-assured early diagnosis of sporadic breast cancer : Update on screening effects and scientific studies].

Background: A quality-assured mammography screening programme has been available since 2009, nationwide, to all women in Germany between the ages of 50 and 69. The programme is based on the European Guidelines. In this review article the authors summarize the current status of scientific assessments of this national early detection programme for breast cancer and provide an outlook regarding ongoing studies on effectiveness tests and further development.

[Time trend in programme sensitivity within the German mammography screening programme in North Rhine-Westphalia and Lower Saxony].

Background: The programme sensitivity is a performance indicator for evaluating the quality of the mammography screening programme (MSP).

Objectives: We analysed the development of the programme sensitivity over time in two federal states of Germany, North Rhine-Westphalia (NRW) and Lower Saxony (NDS).

Materials And Methods: Data from 2,717,801 (NRW) and 1,197,660 (NDS) screening examinations between 2006 and 2011 were linked with data of the State Cancer Registry NRW and the Epidemiological Cancer Registry NDS, respectively.

Digital breast tomosynthesis plus synthesised images versus standard full-field digital mammography in population-based screening (TOSYMA): protocol of a randomised controlled trial.

Introduction: Development of digital breast tomosynthesis (DBT) provides a technology that generates three-dimensional data sets, thus reducing the pitfalls of overlapping breast tissue. Observational studies suggest that the combination of two-dimensional (2D) digital mammography and DBT increases diagnostic accuracy. However, because of duplicate exposure, this comes at the cost of an augmented radiation dose.