Strategies for Decreasing Screening Mammography Recall Rates While Maintaining Performance Metrics

Rationale and Objective

This study aimed to determine the impact of interventions designed to reduce screening mammography recall rates on screening performance metrics.

Materials and Methods

We assessed baseline performance for full-field digital mammography (FFDM) and digital breast tomosynthesis mammography (DBT) for a 3-year period before intervention. The first intervention sought to increase awareness of recalls from screening mammography. Breast imagers discussed their perceptions regarding screening recalls and were required to review their own recalled cases, including outcomes of diagnostic evaluation and biopsy. The second intervention implemented consensus double reading of all recalls, requiring two radiologists to agree if recall was necessary. Recall rates, cancer detection rates, and positive predictive value 1 (PPV1) were compared before and after each intervention.

Results

The baseline recall rate, cancer detection rate, and PPV1 were 11.1%, 3.8/1000, and 3.4%, respectively, for FFDM, and 7.6%, 4.8/1000, and 6.0%, respectively, for DBT. Recall rates decreased significantly to 9.2% for FFDM and to 6.6% for DBT after the first intervention promoting awareness, as well as to 9.9% for FFDM after the second intervention implementing group consensus. PPV1 increased significantly to 5.7% for FFDM and to 9.0% for DBT after the second intervention. Cancer detection rate did not significantly change with the implementation of these interventions. An average of 2.3 minutes was spent consulting for each recall.

Conclusion

Reduction in recall rates is desirable, provided performance metrics remain favorable. Our interventions improved performance and could be implemented in other breast imaging settings.

Introduction

Breast cancer is the most common cancer affecting women worldwide, with approximately 500,000 annual deaths due to disease-specific mortality. An estimated 246,660 new cases of invasive breast cancer will be diagnosed in the United States alone, with 40,450 deaths from the disease in 2016 . Early detection with screening mammography leads to improved survival and less aggressive treatment, decreasing mortality from breast cancer by an estimated 19%–40% depending on age and breast tissue density .

Screening mammography benefits women with earlier detection, but less desirable outcomes may occur if a woman is recalled for additional evaluation and the result is ultimately benign. After a recall from screening mammography, a benign interpretation may occur with additional imaging alone. However, in some cases, large core needle biopsy or even surgery may be required to prove benignity. False-positive screening recalls and the resulting additional evaluation may cause significant psychological distress to women .

Get Radiology Tree app to read full this article<

Materials and Methods

Get Radiology Tree app to read full this article<

Results

Get Radiology Tree app to read full this article<

TABLE 1

Performance Metrics by Modality and Study Period

Modality and Metrics Study Period Baseline Awareness Consensus FFDM_n_ = 47,767n = 4296n = 2900 Recall rate 11.1% 9.2% \* 9.9% \* Cancer detection † 3.8 3.1 5.9 PPV1 3.4% 3.1% 5.7% \* DBT_n_ = 7772n = 7287n = 9190 Recall rate 7.6% 6.6% \* 7.2% Cancer detection 4.8 6.2 5.7 PPV1 6.0% 10.8% \* 9.0% \*

DBT, digital breast tomosynthesis; FFDM, full-field digital mammography; PPV1, positive predictive value 1.

Get Radiology Tree app to read full this article<

TABLE 2

Performance Metrics Stratified by Age Group

Age Group, Modality, and Metrics Study Period Baseline Awareness Consensus Women aged 40–49 y FFDM_n_ = 11,002n = 1124n = 736 Recall rate 15.0% 12.7% \* 13.2% Cancer detection † 1.7 0.90 1.4 PPV1 1.0% 0.6% 0.9% DBT_n_ = 1428n = 1421n = 1954 Recall rate 10.7% 9.8% 11.1% Cancer detection 1.4 3.6 4.7 PPV1 1.2% 3.8% \* 4.7% \* Women aged 50–64 y FFDM_n_ = 22,322n = 2192n = 1537 Recall rate 10.6% 8.9% \* 9.5% Cancer detection 4.0 4.1 4.6 PPV1 3.8% 4.3% 5.2% \* DBT_n_ = 3823n = 3173n = 4068 Recall rate 7.4% 6.2% 6.4% Cancer detection 5.0 4.8 4.2 PPV1 6.4% 8.8% \* 6.8% Women aged ≥65 y FFDM_n_ = 13,840n = 928n = 593 Recall rate 8.5% 5.7% \* 6.4% Cancer detection 5.0 3.2 15.2 PPV1 6.0% 5.4% 21.4% \* DBT_n_ = 2451n = 2627n = 3096 Recall rate 6.4% 5.4% 5.5% Cancer detection 6.6 9.6 8.1 PPV1 10.5% 20.8% \* 17.7% \*

DBT, digital breast tomosynthesis; FFDM, full-field digital mammography; PPV1, positive predictive value 1.

Get Radiology Tree app to read full this article<

Discussion

Get Radiology Tree app to read full this article<

Conclusion

Get Radiology Tree app to read full this article<

Acknowledgments

Get Radiology Tree app to read full this article<

References

1. American Cancer Society : Cancer facts and figures, 3rd ed. Available at: www.cancer.org
2. American Cancer Society : Global cancer facts and figures. 3rd ed. Available at: www.cancer.org
3. Nelson H.D., Pappas M., Cantor A., et. al.: Harms of breast cancer screening: systematic review to update the 2009 U.S. Preventive Services Task Force recommendation. Ann Intern Med 2016; 164: pp. 256-267.
4. Oeffinger K.C., Fontham E.T., Etzioni R., et. al.: Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 2015; 314: pp. 1599-1614.
5. Smith R.A., Duffy S.W., Gabe R., et. al.: The randomized trials of breast cancer screening: what have we learned?. Radiol Clin North Am 2004; 42: pp. 793-806.
6. Bond M., Pavey T., Welch K., et. al.: Systematic review of the psychological consequences of false-positive screening mammograms. Health Technol Assess 2013; 17: pp. 1-170. v-vi
7. Brodersen J., Siersma V.D.: Long-term psychosocial consequences of false-positive screening mammography. Ann Fam Med 2013; 11: pp. 106-115.
8. Schou Bredal I., Karesen R., Skaane P., et. al.: Recall mammography and psychological distress. Eur J Cancer 2013; 49: pp. 805-811.
9. Myers E.R., Moorman P., Gierisch J.M., et. al.: Benefits and harms of breast cancer screening: a systematic review. JAMA 2015; 314: pp. 1615-1634.
10. O’Donoghue C., Eklund M., Ozanne E.M., et. al.: Aggregate cost of mammography screening in the United States: comparison of current practice and advocated guidelines. Ann Intern Med 2014; 160: pp. 145-1217.
11. Bonafede M., Miller J., Lenhart G., et. al.: Health insurer burden of patient recall following breast cancer screening mammography. International Society for Pharmacoeconomics and Outcomes Research, 19th Annual International Meeting2014.
12. Alcusky M., Philpotts L., Bonafede M., et. al.: The patient burden of screening mammography recall. J Womens Health (Larchmt) 2014; 23: pp. S11-S19.
13. Perry N., Broeders M., de Wolf C., et. al.: European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition—summary document. Ann Oncol 2008; 19: pp. 614-622.
14. Brown J., Bryan S., Warren R.: Mammography screening: an incremental cost effectiveness analysis of double versus single reading of mammograms. BMJ 1996; 312: pp. 809-812.
15. Dinnes J., Moss S., Melia J., et. al.: Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: findings of a systematic review. Breast 2001; 10: pp. 455-463.
16. Klompenhouwer E.G., Weber R.J., Voogd A.C., et. al.: Arbitration of discrepant BI-RADS 0 recalls by a third reader at screening mammography lowers recall rate but not the cancer detection rate and sensitivity at blinded and non-blinded double reading. Breast 2015; 24: pp. 601-607.
17. Skaane P.: Breast cancer screening with digital breast tomosynthesis. Breast Cancer 2017; 24: pp. 32-41.
18. Sumkin J.H., Ganott M.A., Chough D.M., et. al.: Recall rate reduction with tomosynthesis during baseline screening examinations: an assessment from a prospective trial. Acad Radiol 2015; 22: pp. 1477-1482.
19. Buist D.S., Anderson M.L., Smith R.A., et. al.: Effect of radiologists’ diagnostic work-up volume on interpretive performance. Radiology 2014; 273: pp. 351-364.

Strategies for Decreasing Screening Mammography Recall Rates While Maintaining Performance Metrics

Rationale and Objective

Materials and Methods

Results

Conclusion

Introduction

Materials and Methods

Results

Discussion

Conclusion

Acknowledgments

References

Further Reading

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy

Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts

CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites