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Diagnostic mammography: identifying minimally acceptable interpretive performance criteria. | LitMetric

AI Article Synopsis

  • The study aimed to create criteria that define the minimum acceptable performance for physicians interpreting diagnostic mammography, focusing on both abnormal screening exams and breast lump evaluations.
  • Using an Angoff method and data from the Breast Cancer Surveillance Consortium, the researchers set performance thresholds for various diagnostic metrics including sensitivity, specificity, and cancer diagnosis rates.
  • The proposed thresholds suggest that improving underperforming physicians could lead to diagnosing significantly more cancers while also decreasing the number of false-positive results in screenings.

Article Abstract

Purpose: To develop criteria to identify thresholds for the minimally acceptable performance of physicians interpreting diagnostic mammography studies.

Materials And Methods: In an institutional review board-approved HIPAA-compliant study, an Angoff approach was used to set criteria for identifying minimally acceptable interpretive performance for both workup after abnormal screening examinations and workup of a breast lump. Normative data from the Breast Cancer Surveillance Consortium (BCSC) was used to help the expert radiologist identify the impact of cut points. Simulations, also using data from the BCSC, were used to estimate the expected clinical impact from the recommended performance thresholds.

Results: Final cut points for workup of abnormal screening examinations were as follows: sensitivity, less than 80%; specificity, less than 80% or greater than 95%; abnormal interpretation rate, less than 8% or greater than 25%; positive predictive value (PPV) of biopsy recommendation (PPV2), less than 15% or greater than 40%; PPV of biopsy performed (PPV3), less than 20% or greater than 45%; and cancer diagnosis rate, less than 20 per 1000 interpretations. Final cut points for workup of a breast lump were as follows: sensitivity, less than 85%; specificity, less than 83% or greater than 95%; abnormal interpretation rate, less than 10% or greater than 25%; PPV2, less than 25% or greater than 50%; PPV3, less than 30% or greater than 55%; and cancer diagnosis rate, less than 40 per 1000 interpretations. If underperforming physicians moved into the acceptable range after remedial training, the expected result would be (a) diagnosis of an additional 86 cancers per 100,000 women undergoing workup after screening examinations, with a reduction in the number of false-positive examinations by 1067 per 100,000 women undergoing this workup, and (b) diagnosis of an additional 335 cancers per 100,000 women undergoing workup of a breast lump, with a reduction in the number of false-positive examinations by 634 per 100,000 women undergoing this workup.

Conclusion: Interpreting physicians who fall outside one or more of the identified cut points should be reviewed in the context of an overall assessment of all their performance measures and their specific practice setting to determine if remedial training is indicated.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632803PMC
http://dx.doi.org/10.1148/radiol.12121216DOI Listing

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