AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

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. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Early screening outcomes before, during, and after a randomized controlled trial with digital breast tomosynthesis.

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. Odds ratios (OR) with 95% CIs was calculated for recall and cancer detection rates. RESULTS: Rate of screen-detected cancer was 0.90% for women screened with DBT in To-Be 2 and 0.64% for DM in pre-To-Be 1. The rates did not differ for the To-Be 1 DM (0.61%), To-Be 1 DBT (0.66%) and post-To-Be 2 DM (0.67%) cohorts. The interval cancer rates ranged between 0.13% and 0.20%. The distribution of histopathologic tumor characteristics did not differ between the cohorts. CONCLUSIONS: Screening all women with DBT following a randomized controlled trial in an organized, population-based screening program showed a temporary increase in the rate of screen-detected cancer.

Volumetric Mammographic Density, Age-Related Decline, and Breast Cancer Risk Factors in a National Breast Cancer Screening Program.

Background: Volumetric mammographic density (VMD) measures can be obtained automatically, but it is not clear how these relate to breast cancer risk factors.Methods: 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.Results: The associations between VMD and most breast cancer risk factors were modest, although highly significant. BMI was positively associated with absolute VMD, whereas inversely associated with percent VMD. Percent VMD was inversely associated with a 5-year older age at screening in premenopausal and postmenopausal women (-0.18% vs. -0.08% for percent VMD and -0.11 cm3 vs. -0.03 cm3 for absolute VMD). This difference was largest among postmenopausal women with BMI < 25 kg/m2 (P for interaction with percent VMD < 0.0001), never users of postmenopausal hormone therapy (P for interaction < 0.0001), and premenopausal women with a family history of breast cancer (P for interaction with absolute VMD = 0.054).Conclusions: VMD is associated with several breast cancer risk factors, the strongest being BMI, where the direction of the association differs for percent and absolute VMD. The inverse association with age appears modified by menopausal status and other breast cancer risk factors.Impact: Because VMD methods are becoming widely available in screening and clinical settings, the association between VMD measures and breast cancer risk factors should be investigated further in longitudinal studies. Cancer Epidemiol Biomarkers Prev; 27(9); 1065-74. ©2018 AACR.