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. Model score distributions were visualised with histograms. We computed the proportion and accompanying 95% confidence intervals (CI) of retrospectively visible and true interval cancers detected and correctly localised by AI at different operating points representing recall rates < 3%. Clinicopathological characteristics of retrospectively visible cancers detected by AI and not were compared using the Chi-squared test and binary logistic regression. RESULTS: Following radiological review, 15.6% of the interval cancer cases were categorised as FN, 19.5% MS, 11.4% occult, and 53.4% true interval cancers. At an operating point of 99.0% specificity, AI could detect and correctly localise 27.5% (95% CI: 23.3-32.3%), and 12.2% (95% CI: 9.5-15.5%) of the FN and MS cases on the prior mammogram, respectively. 228 of these retrospectively visible cases were advanced/metastatic at diagnosis; 21.1% (95% CI: 16.3-26.8%) were found by AI on the screening mammogram. Increased likelihood of detection of retrospectively visible cancers with AI was observed for lower-grade carcinomas and those with involved lymph nodes at diagnosis. Among true interval cancers, AI could detect and correctly localise in the screening mammogram where subsequent malignancies would appear in 2.8% (95% CI: 2.0-3.9%) of cases. CONCLUSIONS: AI can support radiologists by detecting a greater number of carcinomas, subsequently decreasing the interval cancer rate and the number of advanced and metastatic cancers.

[Systematic and quality-assured early diagnosis of sporadic breast cancer : Update on screening effects and scientific studies]. / Systematische und qualitätsgesicherte Früherkennung des sporadischen Mammakarzinoms : Update Screening-Effekte und wissenschaftliche Studien.

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. RESULTS: We expect a decline in mortality rates relating to breast cancer as a result of successfully bringing diagnoses forward and a decrease in advanced breast cancer after a repeated screening. The extent will be shown in the current ZEBra study on mortality evaluation. CONCLUSION: Potential for a further increase in the effectiveness of the systematic early detection of breast cancer can be identified in four areas: (1) More women should take advantage of the early detection opportunities offered by the medical insurance funds; so far, on average, only about 50% of the women between 50 and 69 who are entitled to a screening examination actually take part in the programme. (2) Entitlement to take part in the programme should be extended to women over 70. (3) The further development of digital mammography towards digital breast tomosynthesis promises to reduce the number of false positive recalls while at the same time increasing sensitivity. (4) There should be scientific studies relating to an extension of screening strategies for the small number of women in the entitlement range who have extremly dense breasts.

[Time trend in programme sensitivity within the German mammography screening programme in North Rhine-Westphalia and Lower Saxony]. / Zeitliche Entwicklung der Programmsensitivität des deutschen Mammographie-Screening-Programms in Nordrhein-Westfalen und Niedersachsen.

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. Breast cancers (invasive and in situ) were either detected at screening or diagnosed within the 24-month interval after an inconspicuous screening result outside the programme. The crude and age-standardized programme sensitivity was calculated per calendar year. The German mammography screening office provided aggregated recall rates. RESULTS: The age-standardized programme sensitivity increased markedly for initial screening examinations from 2006 to 2011 from 75.0% (95% CI: 72.1-77.9) to 80.5% (95% CI: 78.5-82.5) in NRW, and from 74.9% (95% CI: 71.4-78.5) to 84.7% (95% CI: 81.1-88.3) in NDS. Concurrently, recall rates increased as well. For subsequent screening examinations, the programme sensitivity increased from 2008 to 2011 from 68.1% (95% CI: 63.1-73.1) to 71.9% (95% CI: 70.2-73.6) in NRW, and from 69.8% (95% CI: 64.2-75.4) to 74.9% (95% CI: 72.3-77.5) in NDS, whereas the recall rates remained relatively constant. CONCLUSIONS: In both federal states, the programme sensitivity increased over time. This increase, possibly indicating an improved quality of diagnosis within the MSP as a learning system, is discussed under consideration of the age distribution of screening participants and the recall rates.

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. This undesired adverse impact can be avoided by using synthesised 2D images reconstructed from the DBT data (s2D).We designed a diagnostic superiority trial on a high level of evidence with the aim of providing a comparison of screening efficacy parameters resulting from DBT+s2D versus the current screening standard 2D full-field digital mammography (FFDM) in a multicentre and multivendor setting on the basis of the quality-controlled, population-based, biennial mammography screening programme in Germany. METHODS AND ANALYSIS: 80 000 women in the eligible age 50-69 years attending the routine mammography screening programme and willing to participate in the TOSYMA trial will be assigned by 1:1 randomisation to either the intervention arm (DBT+s2D) or the control arm (FFDM) during a 12-month recruitment period in screening units of North Rhine-Westphalia and Lower Saxony. State cancer registries will provide the follow-up of interval cancers.Primary endpoints are the detection rate of invasive breast cancers at screening examination and the cumulative incidence of interval cancers in the 2 years after a negative examination. Secondary endpoints are the detection rate of ductal carcinoma in situ and of tumour size T1, the recall rate for assessment, the positive predictive value of recall and the cumulative 12-month incidence of interval cancers. An adaptive statistical design with one interim analysis provides the option to modify the design. ETHICS AND DISSEMINATION: This protocol has been approved by the local medical ethical committee (2016-132-f-S). Results will be submitted to international peer-reviewed journals. TRIAL REGISTRATION: NCT03377036; Pre-results.