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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Artificial Intelligence Versus Radiologist False-Positives on Digital Breast Tomosynthesis Examinations in a

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Artificial intelligence (AI) and radiologists had similar false-positive rates in mammography screening. AI-only false positives were more common in older women with prior breast procedures, while radiologist-only false positives often involved masses.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • False-positive findings in mammography artificial intelligence (AI) can inform strategies to reduce recall rates.
  • Understanding AI's false-positive characteristics is crucial for its clinical integration in breast cancer screening.

Purpose of the Study:

  • Compare the characteristics of false-positive digital breast tomosynthesis (DBT) examinations between AI and radiologists.
  • Evaluate AI's performance against radiologists in identifying false positives during breast cancer screening.

Main Methods:

  • Retrospective analysis of 3183 screening DBT examinations from 2977 women (mean age 58).
  • A commercial AI tool analyzed DBT images; radiologists provided interpretations.
  • False positives were defined as no breast cancer diagnosis within 1 year; radiologists re-reviewed AI-flagged findings.

Main Results:

  • Both AI and radiologists had a 10% false-positive rate.
  • AI-only false positives were associated with older age, fewer dense breasts, and more prior breast cancer history/procedures compared to radiologist-only false positives.
  • Concordant false-positive findings between AI and radiologists had a high rate (44%) of yielding high-risk lesions upon biopsy.

Conclusions:

  • Significant differences exist in patient and imaging characteristics between AI and radiologist false-positive DBT findings.
  • While overlap is small, concordant false positives represent a potentially enriched subset of actionable abnormalities.
  • Findings can guide AI implementation to enhance DBT recall specificity in breast cancer screening.