Data analysis for detection and localization of multiple abnormalities with application to mammography

Acad Radiol. 2000 Jul;7(7):516-25. doi: 10.1016/s1076-6332(00)80324-4.


Rationale and objectives: In assessing diagnostic accuracy it is often essential to determine the reader's ability both to detect and to correctly locate multiple abnormalities per patient. The authors developed a new approach for the detection and localization of multiple abnormalities and compared it with other approaches.

Materials and methods: The new approach involves partitioning the image into multiple regions of interest (ROIs). The reader assigns a confidence score to each ROI. Statistical methods for clustered data are used to assess and compare reader accuracy. The authors applied this new method to a reader-performance study of conventional film images and digitized images used to detect and locate malignant breast cancer lesions.

Results: The ROI-based approach, the free-response receiver operating characteristic (FROC) curve, and the patient-based approach handle the estimation of the false-positive rate (FPR) quite differently. These differences affect the measures of the respective areas under the curves. In the ROI-based approach the denominator is the number of ROIs without a malignant lesion. In the FROC approach the average number of false-positive findings per patient is plotted on the x axis of the curve. In contrast, the patient-based approach mishandles the FPR by ignoring multiple detection and/or localization errors in the same patient. The FROC approach does not lend itself easily to statistical evaluations.

Conclusion: The ROI-based approach appropriately captures both the detection and localization tasks. The interpretation of the ROI-based accuracy measures is simple and clinically relevant. There are statistical methods for estimating and comparing ROI-based estimates of accuracy.

Publication types

  • Comparative Study

MeSH terms

  • Breast Neoplasms / diagnostic imaging*
  • Female
  • Humans
  • Mammography / methods*
  • Mammography / statistics & numerical data
  • Observer Variation
  • ROC Curve
  • Radiographic Image Enhancement
  • Sensitivity and Specificity