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Congress: ECR25
Poster Number: C-18295
Type: Poster: EPOS Radiologist (scientific)
Authorblock: M. B. Bergan, M. Larsen, J. Gjesvik, Å. S. Holen, N. Moshina, T. Hovda, H. W. Koch, M. A. Martiniussen, S. Hofvind; Oslo/NO
Disclosures:
Marie Burns Bergan: Nothing to disclose
Marthe Larsen: Nothing to disclose
Jonas Gjesvik: Nothing to disclose
Åsne Sørlien Holen: Nothing to disclose
Nataliia Moshina: Nothing to disclose
Tone Hovda: Nothing to disclose
Henrik Wethe Koch: Nothing to disclose
Marit Almenning Martiniussen: Nothing to disclose
Solveig Hofvind: Nothing to disclose
Keywords: Breast, Mammography, Computer Applications-Detection, diagnosis, Cancer
Methods and materials

This retrospective cohort study included 1,027,430 screening examinations, including 5785 screen-detected cancers, performed in BreastScreen Norway, 2004-2021. All examinations were independently interpreted by two breast radiologists. The radiologists scored each breast from 1 to 5, where 1 indicated negative for abnormality; 2, probably benign; 3, intermediate suspicion; 4, probably malignant; 5, high suspicion of malignancy (1). All examinations were processed by the AI system Lunit INSIGHT MMG version 1.1.7.2, which provided a continuous malignancy score from 0 to 100, with higher scores indicating higher risk of breast cancer. Cancer cases (screen-detected and interval cancers) among the top 3%, 5% and 10% of examinations with the highest AI scores was presented for combinations of interpretation scores given by the two radiologists.

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