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Birdwell, Robyn

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Birdwell

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Robyn

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Birdwell, Robyn
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    If You Don’t Find It Often, You Often Don’t Find It: Why Some Cancers Are Missed in Breast Cancer Screening
    (Public Library of Science, 2013) Evans, Karla K.; Birdwell, Robyn; Wolfe, Jeremy
    Mammography is an important tool in the early detection of breast cancer. However, the perceptual task is difficult and a significant proportion of cancers are missed. Visual search experiments show that miss (false negative) errors are elevated when targets are rare (low prevalence) but it is unknown if low prevalence is a significant factor under real world, clinical conditions. Here we show that expert mammographers in a real, low-prevalence, clinical setting, miss a much higher percentage of cancers than are missed when the mammographers search for the same cancers under high prevalence conditions. We inserted 50 positive and 50 negative cases into the normal workflow of the breast cancer screening service of an urban hospital over the course of nine months. This rate was slow enough not to markedly raise disease prevalence in the radiologists’ daily practice. Six radiologists subsequently reviewed all 100 cases in a session where the prevalence of disease was 50%. In the clinical setting, participants missed 30% of the cancers. In the high prevalence setting, participants missed just 12% of the same cancers. Under most circumstances, this low prevalence effect is probably adaptive. It is usually wise to be conservative about reporting events with very low base rates (Was that a flying saucer? Probably not.). However, while this response to low prevalence appears to be strongly engrained in human visual search mechanisms, it may not be as adaptive in socially important, low prevalence tasks like medical screening. While the results of any one study must be interpreted cautiously, these data are consistent with the conclusion that this behavioral response to low prevalence could be a substantial contributor to miss errors in breast cancer screening.
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    Breast cancer screening in the era of density notification legislation: summary of 2014 Massachusetts experience and suggestion of an evidence-based management algorithm by multi-disciplinary expert panel
    (Springer Science + Business Media, 2015) Freer, Phoebe E.; Slanetz, Priscilla; Haas, Jennifer; Tung, Nadine; Hughes, Kevin; Armstrong, Katrina; Semine, A. Alan; Troyan, Susan L.; Birdwell, Robyn
    Purpose: Stemming from breast density notification legislation in Massachusetts effective 2015, we sought to develop a collaborative evidence-based approach to density notification that could be used by practitioners across the state. Our goal was to develop an evidence-based consensus management algorithm to help patients and health care providers follow best practices to implement a coordinated, evidence-based, cost-effective, sustainable practice and to standardize care in recommendations for supplemental screening. Methods: We formed the Massachusetts Breast Risk Education and Assessment Task Force (MA-BREAST) a multi-institutional, multi-disciplinary panel of expert radiologists, surgeons, primary care physicians, and oncologists to develop a collaborative approach to density notification legislation. Using evidence-based data from the Institute for Clinical and Economic Review (ICER), the Cochrane review, National Comprehensive Cancer Network (NCCN) guidelines, American Cancer Society (ACS) recommendations, and American College of Radiology (ACR) appropriateness criteria, the group collaboratively developed an evidence-based best-practices algorithm. Results: The expert consensus algorithm uses breast density as one element in the risk stratification to determine the need for supplemental screening. Women with dense breasts and otherwise low risk (<15% lifetime risk), do not routinely require supplemental screening per the expert consensus. Women of high risk (>20% lifetime) should consider supplemental screening MRI in addition to routine mammography regardless of breast density. Conclusion: We report the development of the multi-disciplinary collaborative approach to density notification. We propose a risk stratification algorithm to assess personal level of risk to determine the need for supplemental screening for an individual woman.