The value of computer aided detection system FOR mammography in the breast cancer screening: single-center, prospective, randomized clinical trial

Purpose. To assess the influence of mammography mapping with the help of computer-aided detection system (CAD) MammCheck II of our own design on the relapse-free survival (RFS) in breast cancer (BC) patients detected during the combined (mammographic and ultrasound [US]) screening. Materials and methods. 10732 women aged 40-87 years old (mean age: 52.20±8.63) who performed mammography were randomized to the standard screening group (mammography → US of the dense breasts) or to the group of CAD-assisted screening (mammography → CAD → targeted US of suspicious CAD markings, as well as the standard US of the dense breasts; CAD group). The primary endpoint was the 3-years RFS. Results. Totally, in the standard screening group we identified 230 BCs (4.29%), in the CAD group — 248 BCs (4.62%; p>0.05), including 49 (21.20%) и 88 (35.48%) 0-I stage BCs, respectively (p<0.05). Median of the primary tumor size was significantly lower in the CAD group (18 mm) compared to the standard screening group (25 mm; р<0.05). 3-years RFS was significantly higher (87.90%) in the CAD group compared to the standard screening group (81.20%; р<0.05). Conclusion. Breast US after the previous mammography CAD mapping significantly increases the 3-years RFS of women with combined screening-detected BC.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.

Online or Offline: Does It Matter? A Review of Existing Interpretation Approaches and Their Effect on Screening Mammography Metrics, Patient Satisfaction, and Cost

The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.

The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison

Background Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. Objective The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. Methods We examined appointment data from 12 months of scheduled mammogram appointments, starting in September 2019 when a web and mobile app self-scheduling process for screening mammograms was made available for the Mayo Clinic primary care practice. Patients registered to the Mayo Clinic Patient Online Services could view the schedules and book their mammogram appointment via the web or a mobile app. Self-scheduling required no telephone calls or staff appointment schedulers. We examined uptake (count and percentage of patients utilizing self-scheduling), number of appointment actions taken by self-schedulers and by those using staff schedulers, no-show outcomes, scheduling efficiency, and weekend and after-hours use of self-scheduling. Results For patients who were registered to patient online services and had screening mammogram appointment activity, 15.3% (14,387/93,901) used the web or mobile app to do either some mammogram self-scheduling or self-cancelling appointment actions. Approximately 24.4% (3285/13,454) of self-scheduling occurred after normal business hours/on weekends. Approximately 9.3% (8736/93,901) of the patients used self-scheduling/cancelling exclusively. For self-scheduled mammograms, there were 5.7% (536/9433) no-shows compared to 4.6% (3590/77,531) no-shows in staff-scheduled mammograms (unadjusted odds ratio 1.24, 95% CI 1.13-1.36; P<.001). The odds ratio of no-shows for self-scheduled mammograms to staff-scheduled mammograms decreased to 1.12 (95% CI 1.02-1.23; P=.02) when adjusted for age, race, and ethnicity. On average, since there were only 0.197 staff-scheduler actions for each finalized self-scheduled appointment, staff schedulers were rarely used to redo or “clean up” self-scheduled appointments. Exclusively self-scheduled appointments were significantly more efficient than staff-scheduled appointments. Self-schedulers experienced a single appointment step process (one and done) for 93.5% (7553/8079) of their finalized appointments; only 74.5% (52,804/70,839) of staff-scheduled finalized appointments had a similar one-step appointment process (P<.001). For staff-scheduled appointments, 25.5% (18,035/70,839) of the finalized appointments took multiple appointment steps. For finalized appointments that were exclusively self-scheduled, only 6.5% (526/8079) took multiple appointment steps. The staff-scheduled to self-scheduled odds ratio of taking multiple steps for a finalized screening mammogram appointment was 4.9 (95% CI 4.48-5.37; P<.001). Conclusions Screening mammograms can be efficiently self-scheduled but may be associated with a slight increase in no-shows. Self-scheduling can decrease staff scheduler work and can be convenient for patients who want to manage their appointment scheduling activity after business hours or on weekends.

At Which Mean Glandular Dose Does the Benefit of Breast Cancer Deaths Averted Equal the Risk of Lives Lost to Screening From Radiation-induced Malignancy for Mammography With and Without Tomosynthesis?

Objective To estimate benefit-to-radiation-risk mean glandular dose (MGD) equivalence values for screening mammography, defined as the yearly MGD (over a 10-year period) at which the estimated benefit of mammography in terms of deaths averted equals the estimated risk of lives lost to screening due to radiation exposure (a benefit-to-risk ratio of 1). Methods Benefit-to-risk ratios were calculated as the ratio of breast cancer deaths averted and lives lost to screening over 10-year intervals starting at age 40 for mammography and tomosynthesis using previously published methodology. The MGD values at which estimated benefit equals risk were tabulated. Results The MGD values at which benefit-to-risk equivalence points were met for digital screening mammography are 63 milligray (mGy) (ages 40–49), 88 mGy (ages 50–59), 176 mGy (ages 60–69), and 336 mGy (ages 70–79). The MGD values that met benefit-to-risk equivalence for screening tomosynthesis plus digital mammography or synthetic mammography are 80 mGy (ages 40–49), 111 mGy (ages 50–59), 224 mGy (ages 60–69), and 427 mGy (ages 70–79). Conclusion Cutoff MGD values at which the estimated benefit from screening equals the estimated risk are well above standard screening MGD exposures. Care is necessary to ensure that threshold values are not exceeded during a screening exam, particularly for women ages 40–49 years old when using digital mammography plus tomosynthesis (due to an approximate doubling of dose per exam that will more readily exceed cutoff MGD values) and when many additional views are obtained.