Digital Mammography In Breast Cancer Screening Research Articles

Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study.

To investigate whether artificial intelligence-based computer-aided diagnosis (AI-CAD) can improve radiologists' performance when used to support radiologists' interpretation of digital mammography (DM) in breast cancer screening. A retrospective database search identified 3158 asymptomatic Korean women who consecutively underwent screening DM between January and December 2019 without AI-CAD support, and screening DM between February and July 2020 with image interpretation aided by AI-CAD in a tertiary referral hospital using single reading. Propensity score matching was used to match the DM with AI-CAD group in a 1:1 ratio with the DM without AI-CAD group according to age, breast density, experience level of the interpreting radiologist, and screening round. Performance measures were compared with the McNemar test and generalized estimating equations. A total of 1579 women who underwent DM with AI-CAD were matched with 1579 women who underwent DM without AI-CAD. Radiologists showed higher specificity (96% [1500 of 1563] vs 91.6% [1430 of 1561]; p < 0.001) and lower abnormal interpretation rates (AIR) (4.9% [77 of 1579] vs 9.2% [145 of 1579]; p < 0.001) with AI-CAD than without. There was no significant difference in the cancer detection rate (CDR) (AI-CAD vs no AI-CAD, 8.9 vs 8.9 per 1000 examinations; p = 0.999), sensitivity (87.5% vs 77.8%; p = 0.999), and positive predictive value for biopsy (PPV3) (35.0% vs 35.0%; p = 0.999) according to AI-CAD support. AI-CAD increases the specificity for radiologists without decreasing sensitivity as a supportive tool in the single reading of DM for breast cancer screening. This study shows that AI-CAD could improve the specificity of radiologists' DM interpretation in the single reading system without decreasing sensitivity, suggesting that it can benefit patients by reducing false positive and recall rates. • In this retrospective-matched cohort study (DM without AI-CAD vs DM with AI-CAD), radiologists showed higher specificity and lower AIR when AI-CAD was used to support decision-making in DM screening. • CDR, sensitivity, and PPV for biopsy did not differ with and without AI-CAD support.

Factors affecting recall rate and false positive fraction in breast cancer screening with breast tomosynthesis – A statistical approach

In this study, we investigate which factors affect the false positive fraction (FPF) for digital breast tomosynthesis (DBT) compared to digital mammography (DM) in a screening population by using classification and regression trees (C&RT) and binary marginal generalized linear models.The data was obtained from the Malmö Breast Tomosynthesis Screening Trial, which aimed to compare the performance of DBT to DM in breast cancer screening. By using data from the first half of the study population (7500 women), a tree with the recall probability for different groups was calculated. The effect of age and breast density on the FPF was estimated using a binary marginal generalized linear model.Our results show that breast density and breast cancer were the main factors influencing recall. The FPF is mainly affected by breast density and increases with breast density for DBT and DM.In conclusion, the results obtained with C&RT are easy to interpret and similar to those obtained using binary marginal generalized linear models. The FPF is approximately 40% higher for DBT compared to DM for all breast density categories.