Transition from film to digital mammography: impact for breast cancer screening through the national breast and cervical cancer early detection program

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Utilization and Cost of Mammography Screening Among Commercially Insured Women 50 to 64 Years of Age in the United States, 2012-2016

Background: In recent years, most insurance plans eliminated cost-sharing for breast cancer screening and recommended screening intervals changed, and newer modalities-digital mammography and breast tomosynthesis-became more widely available. The objectives of this study are to examine how these changes affected utilization, frequency, and costs of breast cancer screening among commercially insured women, and to understand factors associated with utilization and frequency of screening. Materials and Methods: This study used commercial insurance claims data for women 50 to 64 years of age, continuously enrolled in commercial insurance plans during 2012-2016. Results: Of the 685,737 eligible women, 20% were not screened, 40% received annual screening, 24% received biennial screening, and 16% were screened less frequently than recommended during the time period examined. Sociodemographic factors such as age <60 years, rurality, and fee-for-service insurance were associated with low screening utilization. Patients who received annual screening incurred ∼1.78 times higher costs compared to those who received biennial screening during the study period. Digital mammography was the most costly and commonly used modality along with computer-aided detection. Conclusions: Evidence-based interventions to promote screening among women who are screened less frequently are needed along with interventions to move toward biennial screening rather than annual screening. Increasing provider awareness regarding breast cancer screening rates and frequency among various sociodemographic groups is essential to guide provider recommendations and shared decision making. The results of this study can guide targeted public health interventions to reduce barriers to screening, and can also serve as inputs for economic analyses of screening interventions and programs.

National trends of synchronous bilateral breast cancer incidence in the United States

PURPOSE

Increase in breast cancer survivorship, advancements in diagnostic imaging and standardization of contralateral breast screening before breast cancer surgery have resulted in increased detection of contralateral breast cancer (CBC). The aim of this study was to assess national trends of synchronous bilateral breast cancer (sBBC) and metachronous bilateral breast cancer (mBBC) incidence in newly diagnosed breast cancer patients.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database (1973-2014) was used to identify 11,177 women diagnosed with CBC. CBC was classified as sBBC when primary breast cancer in both breasts is diagnosed in the same year, or as mBBC, when diagnosed more than one year from primary breast cancer. Temporal trends in sBBC incidence were then evaluated using the Cochran-Armitage test for trend.

RESULTS

Of the 11,177 women diagnosed with CBC, 4228 (38%) had sBBC and 6949 (62%) had mBBC. The incidence of sBBC increased significantly from 1.4% in 1975 to 2.9% in 2014 (p < 0.001). sBBC was more likely to be diagnosed as early stage in recent years (78% in 1975 vs. 90% in 2014 [p < 0.001]), and 69% of patients were treated with mastectomy in 2014.

CONCLUSION

The number of sBBC has increased, and contralateral tumors are more likely to be detected at an early stage with the first primary breast cancer. Despite the early stage findings, most were treated with mastectomy. Further studies are needed to define the best therapy for patients with contralateral disease and optimal surveillance and detection methods.

Assessing the Accuracy of Caries Diagnosis in Bitewing Radiographs Using Different Reproduction Media

STATEMENT OF THE PROBLEM

After introducing digital radiography, practitioners started reading radiographs from computer monitors; however, many still prefer hard-copy radiographs.

PURPOSE

This study aimed to assess the possible superiority of either type of radiograph recording media (computer monitor, film, or paper) in diagnosis and perception of the depth of the cariogenic lesions.

MATERIALS AND METHOD

Twenty digital bitewing radiographs, obtained from 200 posterior extracted teeth, were displayed on an LG monitor and printed on paper and film using Kodak printers. Two observers independently measured lesions depth on the images. Serial sections of teeth were obtained and the sections were evaluated by a stereomicroscope to determine the actual depth of cariogenic lesions. The efficacy of the each medium was assessed by determining its specificity and sensitivity in comparison with those of histological images. Weighted kappa coefficients and the ROC analysis were used for the statistical analysis.

RESULTS

Strong intra- and inter-observer agreements (0.818 to 0.958, 0.77 to 0.85) were found for all detection methods. The highest Az value was obtained with the monitor-displayed images (Az: 0.879); however, differences between detection methods were not statistically significant (p> 0.05).

CONCLUSION

Monitor-displayed bitewing radiographs, paper, and film prints used in our study performed similarly in the detection of proximal caries.

Improved Screening Mammogram Workflow by Maximizing PACS Streamlining Capabilities in an Academic Breast Center

The aim of this study was to perform an operational improvement project targeted at the breast imaging reading workflow of mammography examinations at an academic medical center with its associated breast centers and satellite sites. Through careful analysis of the current workflow, two major issues were identified: stockpiling of paperwork and multiple worklists. Both issues were considered to cause significant delays to the start of interpreting screening mammograms. Four workflow changes were suggested (scanning of paperwork, worklist consolidation, use of chat functionality, and tracking of case distribution among trainees) and implemented in July 2015. Timestamp data was collected 2 months before (May-Jun) and after (Aug-Sep) the implemented changes. Generalized linear models were used to analyze the data. The results showed significant improvements for the interpretation of screening mammograms. The average time elapsed for time to open a case reduced from 70 to 28 min (60 % decrease, p < 0.001), report turn-around time with preliminary signature decreased from 151 to 107 min (29 % decrease, p < 0.001), and report turn-around time final signature from 153 to 139 min (9 % decrease, p = 0.002). These improvements were achieved while keeping the efficiency of the workflow for diagnostic mammograms at large unaltered even with increased volume of mammography examinations (31 % increase of 4344 examinations for May-Jun to 5678 examinations for Aug-Sep). In conclusion, targeted efforts to improve the breast imaging reading workflow for screening mammograms in a teaching environment provided significant performance improvements without affecting the workflow of diagnostic mammograms.

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.

Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model

BACKGROUND

The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications.

METHODS

The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples.

RESULTS

The model results consistently match key temporal trends in US breast cancer incidence and mortality.

CONCLUSION

The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD)

PURPOSE

Evaluate concurrent Computer-Aided Detection (CAD) with Digital Breast Tomosynthesis (DBT) to determine impact on radiologist performance and reading time.

MATERIALS AND METHODS

The CAD system detects and extracts suspicious masses, architectural distortions and asymmetries from DBT planes that are blended into corresponding synthetic images to form CAD-enhanced synthetic images. Review of CAD-enhanced images and navigation to corresponding planes to confirm or dismiss potential lesions allows radiologists to more quickly review DBT planes. A retrospective, crossover study with and without CAD was conducted with six radiologists who read an enriched sample of 80 DBT cases including 23 malignant lesions in 21 women. Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) compared the readings with and without CAD to determine the effect of CAD on overall interpretation performance. Sensitivity, specificity, recall rate and reading time were also assessed. Multi-reader, multi-case (MRMC) methods accounting for correlation and requiring correct lesion localization were used to analyze all endpoints. AUCs were based on a 0-100% probability of malignancy (POM) score. Sensitivity and specificity were based on BI-RADS scores, where 3 or higher was positive.

RESULTS

Average AUC across readers without CAD was 0.854 (range: 0.785-0.891, 95% confidence interval (CI): 0.769,0.939) and 0.850 (range: 0.746-0.905, 95% CI: 0.751,0.949) with CAD (95% CI for difference: -0.046,0.039), demonstrating non-inferiority of AUC. Average reduction in reading time with CAD was 23.5% (95% CI: 7.0-37.0% improvement), from an average 48.2 (95% CI: 39.1,59.6) seconds without CAD to 39.1 (95% CI: 26.2,54.5) seconds with CAD. Per-patient sensitivity was the same with and without CAD (0.865; 95% CI for difference: -0.070,0.070), and there was a small 0.022 improvement (95% CI for difference: -0.046,0.089) in per-lesion sensitivity from 0.790 without CAD to 0.812 with CAD. A slight reduction in specificity with a -0.014 difference (95% CI for difference: -0.079,0.050) and a small 0.025 increase (95% CI for difference: -0.036,0.087) in recall rate in non-cancer cases were observed with CAD.

CONCLUSIONS

Concurrent CAD resulted in faster reading time with non-inferiority of radiologist interpretation performance. Radiologist sensitivity, specificity and recall rate were similar with and without CAD.