Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI

Acad Radiol. 2009 Sep;16(9):1064-9. doi: 10.1016/j.acra.2009.03.018. Epub 2009 Jun 9.

Abstract

Rationale and objectives: To evaluate the effect that variations in the enhancement threshold have on the diagnostic accuracy of two computer-aided detection (CAD) systems for magnetic resonance based breast cancer screening.

Materials and methods: Informed consent was obtained from all patients participating in cancer screening and this study was approved by the participating institution's review board. This retrospective study was nested in a prospective, single-institution, high-risk, breast screening study involving dynamic contrast-enhanced magnetic resonance imaging. Only those screening examinations (n = 223) for which a histopathological diagnosis was available were included. Two CAD methods were performed: the signal enhancement ratio (SER) and support vector machines (SVMs). Statistical analysis was performed by tracking changes in each CAD test's diagnostic accuracy (eg, receiver-operating characteristic [ROC] curve area, maximum possible sensitivity) with changes in the enhancement threshold.

Results: The enhancement threshold plays a significant role in affecting a CAD test's potential sensitivity, ROC curve area, and number of assumed true and false-positive predictions per cancerous examination. A high threshold can also limit the CAD-based detection of the full size of a lesion.

Conclusions: Enhancement thresholds can limit a CAD test's ability to diagnose a lesion's full size and as such should not be raised above 60%. The clinically used SER method exhibits a high rate of false positives at low enhancement thresholds and as such the threshold should not be set lower than 50%. The SVM method yielded better results in our study than the SER method at clinically realistic enhancement thresholds.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Breast Neoplasms / diagnosis*
  • Female
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity