The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Cost-effectiveness of risk-based breast cancer screening: A systematic review

To analyse published evidence on the economic evaluation of risk-based screening (RBS), a full systematic literature review was conducted. After a quality appraisal, we compared the cost-effectiveness of risk-based strategies (low-risk, medium-risk and high-risk) with no screening and age-based screening. Studies were also analysed for modelling, risk stratification methods, input parameters, data sources and harms and benefits. The 10 modelling papers analysed were based on screening performance of film-based mammography (FBM) (three); digital mammography (DM) and FBM (two); DM alone (three); DM, ultrasound (US) and magnetic resonance imaging (one) and DM and US (one). Seven studies did not include the cost of risk-stratification, and one did not consider the cost of diagnosis. Disutility was incorporated in only six studies (one for screening and five for diagnosis). None of the studies reported disutility of risk-stratification (being considered as high-risk). Risk-stratification methods varied from only breast density (BD) to the combination of familial risk, genetic susceptibility, lifestyle, previous biopsies, Jewish ancestry and reproductive history. Less or no screening in low-risk women and more frequent mammography screening in high-risk women was more cost-effective compared to no screening and age-based screening. High-risk women screened annually yielded a higher mortality rate reduction and more quality-adjusted life years at the expense of higher cost and false positives. RBS can be cost effective compared to the alternatives. However, heterogeneity among risk-stratification methods, input parameters, and weaknesses in the methodologies hinder the derivation of robust conclusions. Therefore, further studies are warranted to assess newer technologies and innovative risk-stratification methods.

Risk Stratification for Screening Mammography: Benefits and Harms

OBJECTIVE

The purpose of this article is to compare commonly used breast cancer risk assessment models, describe the machine learning approach and big data in risk prediction, and summarize the potential benefits and harms of restrictive risk-based screening.

CONCLUSION

The commonly used risk assessment models for breast cancer can be complex and cumbersome to use. Each model incorporates different sets of risk factors, which are weighted differently and can produce different results for the same patient. No model is appropriate for all subgroups of the general population and only one model incorporates mammographic breast density. Future development of risk prediction tools that are generalizable and simpler to use are needed in guiding clinical decisions.

Availability of prior mammograms affects incomplete report rates in mobile screening mammography

PURPOSE

Mobile mammography can improve access to screening mammography in rural areas and underserved populations. We evaluated the frequency of incomplete reports in mobile mammography screening and the relationships between prior mammograms and recall rates.

METHODS

The frequency of incomplete mammogram reports, the subgroups of those needing prior comparison mammograms, recalls for additional imaging, and availability of prior mammograms of a mobile screening mammography unit were compared with fixed site mammography from January 1, 2007 through December 31, 2009. All mobile unit mammograms were full field digital mammography (FFDM). Differences between rates of recall, incomplete reports, and availability of prior mammograms were calculated using the Chi-Square statistic.

RESULTS

Of 2640 mobile mammography cases, 21.9% (578) reports were incomplete, versus 15.2% (7653) (p ≤ 0.001) of 50325 fixed site reports. Of incomplete cases, recall for additional imaging occurred among 8.3% (218) of mobile mammography reports versus 11.3% (5708) (p ≤ 0.001) of fixed site reports. Prior mammograms were needed among 13.6% (360) of mobile mammography versus 3.9% (1945) (p ≤ 0.001) of fixed site reports. Mobile mammography recall rate varied with availability of prior mammograms: 16.0% (54) when no prior mammograms, 7.6% (127) when prior mammograms were elsewhere but unavailable and 5.9% (37) when prior FFDM were immediately available (p ≤ 0.001).

CONCLUSIONS

Incomplete reports were more frequent in mobile mammography than the fixed site. The availability of prior comparison mammograms at time of interpretation decreased the rate of incomplete mammogram reports. Recall rates were higher without prior comparison mammograms and lowest when comparison FFDM mammograms were available.

Tuberculosis mastitis presenting as bilateral breast masses

Tuberculosis mastitis can be a challenging diagnosis, often presenting with clinical and imaging findings that are suspicious for malignancy. We present a case of a 49-year-old female with a breast mass initially diagnosed as idiopathic granulomatous mastitis. Failure to respond to standard treatments, development of new breast masses, and discovery of a concurrent ulcerating thigh rash with similar histologic findings as the breast masses prompted further investigation, which ultimately lead to the diagnosis of tuberculosis mastitis. There was rapid resolution of both breast and skin symptoms after initiation of empiric drug therapy.

Paradigm Shift From Halstedian Radical Mastectomy to Personalized Medicine

Breast cancer management changed from radical mastectomy to precision medicine in a period longer than a century. The aims of these changes were to refrain from overdiagnoses and overtreatments as well as their harmful side effects and extra costs. Breast cancer is a heterogeneous disease and characterized by many morphological, clinical and molecular features. We now increasingly realise that a one-size-fits-all strategy does not apply to all breast cancer patients. Personalized medicine may be used for breast cancer screening, diagnosis and treatment. Individualized screening can decrease the number of unnecessary mammograms, additional radiologic studies, breast biopsies and false positivity rates. However, additional 15 to 20 years are necessary to reach the results of prospective randomized trials comparing low-risk and normal-risk women. We also should wait for outcomes of risk-based screening trials. The rates of overtreatment in patients with early-stage breast cancer have reached 40% in many studies. Personalized treatment has succeeded in reducing it substantially by using tumour genetic profiling and tumour receptors in early breast cancer patients. However, it has its limits and it is impossible to generalize it to all patients. New biomarkers and molecular classifications have also led to the development of novel therapies and treatment strategies. And, they can contribute to a more personalized management of breast cancer patients.

Breast Cancer Screening in the Precision Medicine Era: Risk-Based Screening in a Population-Based Trial

Ongoing controversy over the optimal approach to breast cancer screening has led to discordant professional society recommendations, particularly in women age 40 to 49 years. One potential solution is risk-based screening, where decisions around the starting age, stopping age, frequency, and modality of screening are based on individual risk to maximize the early detection of aggressive cancers and minimize the harms of screening through optimal resource utilization. We present a novel approach to risk-based screening that integrates clinical risk factors, breast density, a polygenic risk score representing the cumulative effects of genetic variants, and sequencing for moderate- and high-penetrance germline mutations. We demonstrate how thresholds of absolute risk estimates generated by our prediction tools can be used to stratify women into different screening strategies (biennial mammography, annual mammography, annual mammography with adjunctive magnetic resonance imaging, defer screening at this time) while informing the starting age of screening for women age 40 to 49 years. Our risk thresholds and corresponding screening strategies are based on current evidence but need to be tested in clinical trials. The Women Informed to Screen Depending On Measures of risk (WISDOM) Study, a pragmatic, preference-tolerant randomized controlled trial of annual vs personalized screening, will study our proposed approach. WISDOM will evaluate the efficacy, safety, and acceptability of risk-based screening beginning in the fall of 2016. The adaptive design of this trial allows continued refinement of our risk thresholds as the trial progresses, and we discuss areas where we anticipate emerging evidence will impact our approach.