Trends in Recall, Biopsy, and Positive Biopsy Rates for Screening Mammography in an Academic Practice

Gur, David; Wallace, Luisa P.; Klym, Amy H.; Hardesty, Lara A.; Abrams, Gordon; Shah, Rachit D.; Sumkin, Jules H.

doi:10.1148/radiol.2352040422

Cited by 41 publications

(27 citation statements)

References 20 publications

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“…The Bayesian network appears to be most effective in decreasing the need for recall (substantially reducing BI-RADS category 0 interpretations), which may address problems encountered in the clinical testing of computer-assisted detection (22,23). The Bayesian network classified thousands of false-positive BI-RADS category 0 assessments correctly as negative (Table 4), which may have the potential to decrease patient anxiety and the need for additional testing (whether this may be during the screening or the diagnostic step).…”

Section: Discussionmentioning

confidence: 99%

“…Although results of evaluation of computer-assisted detection performance on the basis of nonconsecutive samples (11)(12)(13)(14)(15)(16) and carefully controlled prospective assessment in clinical practice (17)(18)(19)(20)(21) have been promising, retrospective evaluation of actual clinical performance has demonstrated disappointing results (22,23). In fact, it has been suggested that the suboptimal performance indicates that computer-assisted detection may have unanticipated negative effects on radiologist decision making, perhaps by deferring recall when marks are not present (22,23). Several groups have improved classification of mammographic abnormalities (computerassisted diagnosis) with computer-extracted imaging features (24)(25)(26) or with radiologist-observed features (27)(28)(29)(30)(31) in selected biopsy cases.…”

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confidence: 99%

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Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Burnside

Davis

Chhatwal³

et al. 2009

Radiology

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Purpose:To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. Materials and Methods:The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results:The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P ϭ .002), sensitivity (90.0% vs 85.3%, P Ͻ .001), and specificity (93.0% vs 88.1%, P Ͻ .001). Conclusion:On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Burnside

Davis

Chhatwal³

et al. 2009

Radiology

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“…It is noted that the rate of surgical biopsy in both the USA and the UK are similar to that in Canada such that 0.14% of females screened by US radiologists qualified under the Mammography Quality Standards Act 91 and just over 0.1% of females screened in the NHS breast-screening programme underwent surgical biopsy. 92 Subsequent to breast biopsy in patients aged 40-79 years from the US BCSC registries, 55-85% of pathology results were returned as truly benign (i.e.…”

Section: Risk-benefit Analysis Diagnostic Follow-upmentioning

confidence: 97%

Is the false-positive rate in mammography in North America too high?

Le¹,

Mothersill²,

Seymour³

et al. 2016

BJR

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The practice of investigating pathological abnormalities in the breasts of females who are asymptomatic is primarily employed using X-ray mammography. The importance of breast screening is reflected in the mortality-based benefits observed among females who are found to possess invasive breast carcinoma prior to the manifestation of clinical symptoms. It is estimated that population-based screening constitutes a 17% reduction in the breast cancer mortality rate among females affected by invasive breast carcinoma. In spite of the significant utility that screening confers in those affected by invasive cancer, limitations associated with screening manifest as potential harms affecting individuals who are free of invasive disease. Disease-free and benign tumour-bearing individuals who are subjected to diagnostic work-up following a screening examination constitute a population of cases referred to as false positives (FPs). This article discusses factors contributing to the FP rate in mammography and extends the discussion to an assessment of the consequences associated with FP reporting. We conclude that the mammography FP rate in North America is in excess based upon the observation of overtreatment of in situ lesions and the disproportionate distribution of detriment and benefit among the population of individuals recalled for diagnostic work-up subsequent to screening. To address the excessive incidence of FPs in mammography, we investigate solutions that may be employed to remediate the current status of the FP rate. Subsequently, it can be suggested that improvements in the breast-screening protocol, medical litigation risk, image interpretation software and the implementation of image acquisition modalities that overcome superimposition effects are promising solutions.

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“…Consequently, diagnostic confi rmation often requires an imageguided biopsy procedure. In current clinical practice, 70%-80% of biopsies yield benign lesions ( 10 ).…”

Section: Patientsmentioning

confidence: 99%

Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis

Su¹

2011

Breast Diseases: A Year Book Quarterly

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Trends in Recall, Biopsy, and Positive Biopsy Rates for Screening Mammography in an Academic Practice

Abstract: Observed increase in biopsy rates reinforces the need to carefully select patients for biopsy to achieve efficient, efficacious, and cost-effective programs for early detection of breast cancers.

Cited by 41 publications

References 20 publications

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Is the false-positive rate in mammography in North America too high?

Early-Stage Invasive Breast Cancers: Potential Role of Optical Tomography with US Localization in Assisting Diagnosis

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