Objective of this study is to develop and test a new computer-aided detection (CAD)
scheme with improved region of interest (ROI) segmentation combined with an image
feature extraction framework to improve performance in predicting short-term breast
cancer risk. A dataset involving 570 sets of "prior" negative mammography screening
cases was retrospectively assembled. In the next sequential "current" screening, 285
cases were positive and 285 cases remained negative. A CAD scheme was applied to all
570 "prior" negative images to stratify cases into the high and low risk case group
of having cancer detected in the "current" screening. First, a new ROI segmentation
algorithm was used to automatically remove useless area of mammograms. Second, from
the matched bilateral craniocaudal view images, a set of 43 image features related
to frequency characteristics of ROIs were initially computed from the discrete cosine
transform and spatial domain of the images. Third, a support vector machine model
based machine learning classifier was used to optimally classify the selected optimal
image features to build a CAD-based risk prediction model. The classifier was trained
using a leave-one-case-out based cross-validation method. Applying this improved CAD
scheme to the testing dataset, an area under ROC curve, AUC = 0.70 +/- 0.04, which
was significantly higher than using the extracting features directly from the dataset
without the improved ROI segmentation step (AUC = 0.63 +/- 0.04). This study demonstrated
that the proposed approach could improve accuracy on predicting short-term breast
cancer risk, which may play an important role in helping eventually establish an optimal
personalized breast cancer paradigm.
