Applying a new maximum local asymmetry feature analysis method to improve near-term breast cancer risk prediction

Abstract

Quantitative assessment of mammographic asymmetry has been investigated for breast cancer risk prediction. A new asymmetry feature extraction method was proposed in this study to enhance the risk prediction accuracy of near-term breast cancer. Breast areas in each pair of bilateral mammographic images were divided into several pairs of matched local annular regions and the maximum local asymmetry features (MLAF) were extracted from these regions. Radial basis function network (RBFN) was used to merge these features for breast cancer risk prediction. The dataset included 560 negative subjects. The risk prediction performance was tested using a leave-one-case-out (LOCO) cross-validation method. Area under the receiver operating characteristic curve (AUC) was used as the risk prediction performance evaluation index. AUC = 0.898 ± 0.013 was obtained by using the MLAFs extracted from the annular regions, which was significantly higher than the AUC value of 0.505 ± 0.025 achieved by using global asymmetry features computed from the whole breast regions (p < 0.05, DeLong's test) and much higher than the AUC values of 0.825 ± 0.017 and 0.717 ± 0.021 achieved by using MLAFs extracted from horizontal strip regions and vertical strip regions. The study demonstrated that near-term breast cancer risk prediction could be improved by using the proposed feature extraction method.