Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method

Abstract

Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists' detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on 'high-risk' cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on 'low-risk' cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in 'current' examination and missed in 'prior' examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on 'current' examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on 'prior' examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the 'prior' examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.

Figure 1

Illustration of a new adaptive CAD cueing approach.

Figure 2

Comparson of histograms of average mass size (mm²) computed from two (CC and MLO) view images of 78 masses depicted on the “current” and “prior” examinations.

Figure 3

Illustration of adaptively changing CAD-generated detection scores based on bilateral mammographic density asymmetry score by projecting the original CAD scores into a new scoring reference line (as shown in dashed line).

Figure 4

Example of two masses with different CAD cueing results. Left column are two “current” images of two cases and the right column are two corresponding “prior” images. The true-positive mass regions are circled in all four images. Two “prior” mass regions depicted on the top-right and bottom-right images correspond to “A” and “B” circles as shown in Figure 3, respectively.

Figure 5

Two FROC-type performance curves representing performance of applying our original CAD scheme to our testing dataset with “current” examinations of 78 positive cases and 338 negative cases.

Figure 6

Two FROC-type performance curves representing performance of applying our original CAD scheme to our testing dataset with “prior” examinations of 78 positive cases and “current” examinations of 338 negative cases.

Figure 7

A ROC-type performance curve to classify between the “prior” examinations of 78 positive cases and the “current” examinations of 338 negative cases using the computed bilateral mammographic density asymmetry scores (solid line curve) with the area under ROC curve (AUC = 0.702). The dash line is a reference line with AUC = 0.5.

Figure 8

The relationship between the number of masses cued on two views and the change of scoring projection line slopes.