Objective: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.
Materials And Method: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination.
Purpose: To describe and compare early screening outcomes before, during and after a randomized controlled trial with digital breast tomosynthesis (DBT) including synthetic 2D mammography versus standard digital mammography (DM) (To-Be 1) and a follow-up cohort study using DBT (To-Be 2).
Methods: Retrospective results of 125,020 screening examinations from four consecutive screening rounds performed in 2014-2021 were described and compared for pre-To-Be 1 (DM), To-Be 1 (DM or DBT), To-Be 2 (DBT), and post-To-Be 2 (DM) cohorts. Descriptive analyses of rates of recall, biopsy, screen-detected and interval cancer, distribution of histopathologic tumor characteristics and time spent on image interpretation and consensus were presented for the four rounds including five cohorts, one cohort in each screening round except for the To-Be 1 trail, which included a DBT and a DM cohort.
Volumetric mammographic density (VMD) measures can be obtained automatically, but it is not clear how these relate to breast cancer risk factors. The cohort consisted of 46,428 women (ages 49-71 years) who participated in BreastScreen Norway between 2007 and 2014 and had information on VMD and breast cancer risk factors. We estimated means of percent and absolute VMD associated with age, menopausal status, body mass index (BMI), and other factors.
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