Uptake of Screening Mammogram in West-central Illinois During COVID-19 Pandemic: Lessons Learned

Objectives. The study aims to reveal the trend of mammogram uptake in seventeen rural counties in Illinois to understand how the COVID-19 pandemic is influencing breast cancer screening in the area.Methods. Aggregated data on mammography screening for west central Illinois was provided by the Illinois Hospital Association. Data for 2018 and 2019 was used to determine the typical monthly and annual screenings for the two years before the onset of COVID-19. Then, the two years' data was compared to the 2020 data. The monthly mean values for the aggregated 2018 and 2019 data were generated as the base "year" to compare with the monthly value for 2020. Paired T-Test analysis was used to find if there were any statistically significant differences the years and between the base year and 2020.Results. January 2020 revealed an uptick to 2,921, which is more than the uptake for January 2018 (2700) and January 2019 (2488), and 13% greater than the mean value of 2,594 for the previous two years. This was followed by a gradual decrease in uptake in February 2020 by 4% compared to previous years at a mean of 2518 and a further decline in March (44%), with a drastic fall (98%) by April 2020 at 56 screening mammograms in all 17 counties. The lowest uptake in any three months occurred from March through May 2020. Compared to previous years, increase in uptake was noted across the region in 2020 June (8%) and July (4%) after the pandemic restrictions were relaxed. Overall, the total uptake in 2020 was 15% less than the average annual uptake for 2018-2019 with a deficit of 5,537. There was no statistically significant difference in mammogram uptake across the three years.Conclusion. The findings reveal that there was a significant reduction in uptake during the pandemic restriction period. However, increased uptake during the rest of the year effectively mitigated this reduction to such an extent that there was no statistically significant downturn compared to each of the previous two years. A rising trend in total annual uptake noted in preceding years could have continued without the COVID-19 event.

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.

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients exhibiting suspicious microcalcification in screening mammograms between July 2007 and December 2019. Five pre-trained DCNN models and an ensemble model were used to classify the microcalcifications as either malignant or benign. Approximately one million images from the ImageNet database had been used to train the five DCNN models. Herein, 1121 mammographic images were used for individual model fine-tuning, 198 for validation, and 260 for testing. Gradient-weighted class activation mapping (Grad-CAM) was used to confirm the validity of the DCNN models in highlighting the microcalcification regions most critical for determining the final class. The ensemble model yielded the best AUC (0.856). The DenseNet-201 model achieved the best sensitivity (82.47%) and negative predictive value (NPV; 86.92%). The ResNet-101 model yielded the best accuracy (81.54%), specificity (91.41%), and positive predictive value (PPV; 81.82%). The high PPV and specificity achieved by the ResNet-101 model, in particular, demonstrated the model effectiveness in microcalcification diagnosis, which, in turn, may considerably help reduce unnecessary biopsies.

Joanne Knight Breast Health Cohort at Siteman Cancer Center: A model last cohort

PurposeThe Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening.MethodsWomen were recruited from November 2008 to April 2012. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry and death register. Routine screening mammograms are collected every 1 to 2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources.ResultsCohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74 percent of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person years of follow-up from date of blood draw per participant. 9,997 women continuing in follow-up. Data collected at baseline include breast cancer risk factors, baseline plasma and white blood cells, mammograms prior to baseline, at baseline, and during follow-up. ConclusionThis cohort assembled and followed in a routine clinical screening and care setting that serves a diverse population of women in the St Louis region, now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and breast image features into prospective evaluation for breast cancer etiology and outcomes.

The Impact of Radiologist Screening Mammogram Reading Volume on Performance in the Ontario Breast Screening Program

Purpose: Although some studies have shown increasing radiologists’ mammography volumes improves performance, there is a lack of evidence specific to digital mammography and breast screening program performance targets. This study evaluates the relationship between digital screening volume and meeting performance targets. Methods: This retrospective cohort study included 493 radiologists in the Ontario Breast Screening Program who interpreted 1,762,173 screening mammograms in participants ages 50-90 between 2014 and 2016. Associations between annual screening volume and meeting performance targets for abnormal call rate, positive predictive value (PPV), invasive cancer detection rate (CDR), sensitivity, and specificity were modeled using mixed-effects multivariate logistic regression. Results: Most radiologists read 500-999 (36.7%) or 1,000-1,999 (31.0%) screens annually, and 18.5% read ≥2,000. Radiologists who read ≥2,000 annually were more likely to meet abnormal call rate (OR = 3.85; 95% CI: 1.17-12.61), PPV (OR = 5.36; 95% CI: 2.53-11.34), invasive CDR (OR = 4.14; 95% CI: 1.50-11.46), and specificity (OR = 4.07; 95% CI: 1.89-8.79) targets versus those who read 100-499 screens. Radiologists reading 1,000-1,999 screens annually were more likely to meet PPV (OR = 2.32; 95% CI: 1.22-4.40), invasive CDR (OR = 3.36; 95% CI: 1.49-7.59) and specificity (OR = 2.00; 95% CI: 1.04-3.84) targets versus those who read 100-499 screens. No significant differences were observed for sensitivity. Conclusions: Annual reading volume requirements of 1,000 in Canada are supported as screening volume above 1,000 was strongly associated with achieving performance targets for nearly all measures. Increasing the minimum volume to 2,000 may further reduce the potential limitations of screening due to false positives, leading to improvements in overall breast screening program quality.

Visualization of the Nipple in Profile: Does It Really Affect Selected Outcomes in Organized Mammographic Screening?

Objective To investigate whether having the nipple imaged in profile was associated with breast characteristics or compression parameters, and whether it affected selected outcomes in screening with standard digital mammography or digital breast tomosynthesis. Methods In this IRB-approved retrospective study, results from 87 450 examinations (174 900 breasts) performed as part of BreastScreen Norway, 2016–2019, were compared by nipple in profile status and screening technique using descriptive statistics and generalized estimating equations. Unadjusted and adjusted odds ratios with 95% confidence intervals (95% CIs) were estimated for outcomes of interest, including age, breast volume, volumetric breast density, and compression force as covariates. Results Achieving the nipple in profile versus not in profile was associated with lower breast volume (845.1 cm3 versus 1059.9 cm3, P &lt; 0.01) and higher mammographic density (5.6% versus 4.4%, P &lt; 0.01). Lower compression force and higher compression pressure were applied to breasts with the nipple in profile (106.6 N and 11.5 kPa) compared to the nipple not in profile (110.8 N and 10.5 kPa, P &lt; 0.01 for both). The adjusted odds ratio was 0.95 (95% CI: 0.88–1.02; P = 0.15) for recall and 0.92 (95% CI: 0.77–1.10; P = 0.36) for screen-detected cancer for nipple in profile versus not in profile. Conclusion Breast characteristics and compression parameters might hamper imaging of the nipple in profile. However, whether the nipple was in profile or not on the screening mammograms did not influence the odds of recall or screen-detected cancer, regardless of screening technique.