Digitale Brust-Tomosynthese und Befundung mit künstlicher Intelligenz
Weigel S, Wunderlich P, Sommer A, Czwoydzinski J, Baltzer PAT, Dewald CLA, Preibsch H, Sauer S, Siegmann-Luz KC, Wenkel E, Wilpert C, Blum SFU (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Book Volume: 198
Pages Range: 1271-1281
Journal Issue: 10
DOI: 10.1055/a-2796-5225
Abstract
Background Digital breast tomosynthesis (DBT) increases sensitivity and specificity compared to digital mammography (DM) in the early detection of breast cancer. The potential of artificial intelligence (AI) for mammographic interpretation resulting in workload reduction is increasingly being reported. Method Presentation of the current evidence on the efficacy of DBT versus DM in population-based breast cancer screening and its support by AI. Narrative review with topic-led literature search of comparative studies of DBT and DM and review of the use of AI in PubMed from 01/2016 to 09/2025. Results In 42 international studies, the breast cancer detection rate was significantly higher with DBT + DM(6.4‰) and DBT + synthetic mammography (SM) (7.4 ‰) than with DM (4.7‰). Concordantly, the randomized TOSYMA study reported a higher invasive breast cancer detection rate with DBT + SM (7.1 ‰) versus DM(4.8‰) with a lower false-positive recall rate (first round 15.6 ‰). The positive predictive value (PPV) of recall was consistent with meta-analyses higher (+4.9%), as was the reading time. With a smaller number of AI studies on DBT than DM, a DBT meta-analysis reported a higher sensitivity of AI alone (89%) than by readers (78%), with a lower specificity of AI. DBT with AI-assisted reading compared to human reading alone increased the detection rate in a prospective study by +3.8‰ without a marked change in the recall rate (+0.8%). Conclusion DBT increases breast cancer detection compared to DMwith more favorable process parameters. AI reporting strategies can further increase sensitivity and reduce human workload. The influence of DBT and AI on reducing the interval cancer rate as a measure of efficiency has not yet been proven.
Involved external institutions
Universität Münster (ehem. Westfälische Wilhelms-Universität (WWU))
Germany (DE)
Medizinische Universität Wien
Austria (AT)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Universitätsklinikum Tübingen
Germany (DE)
Universitätsklinikum Würzburg
Germany (DE)
Medizininformatik-Initiative
Germany (DE)
Universitätsklinikum Freiburg
Germany (DE)
Universitätsklinikum Carl Gustav Carus Dresden
Germany (DE)
Germany (DE)
Medizinische Universität Wien
Austria (AT)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Universitätsklinikum Tübingen
Germany (DE)
Universitätsklinikum Würzburg
Germany (DE)
Medizininformatik-Initiative
Germany (DE)
Universitätsklinikum Freiburg
Germany (DE)
Universitätsklinikum Carl Gustav Carus Dresden
Germany (DE)
How to cite
APA:
Weigel, S., Wunderlich, P., Sommer, A., Czwoydzinski, J., Baltzer, P.A.T., Dewald, C.L.A.,... Blum, S.F.U. (2026). Digitale Brust-Tomosynthese und Befundung mit künstlicher Intelligenz. Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 198(10), 1271-1281. https://doi.org/10.1055/a-2796-5225
MLA:
Weigel, Stefanie, et al. "Digitale Brust-Tomosynthese und Befundung mit künstlicher Intelligenz." Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 198.10 (2026): 1271-1281.
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