Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification.

RATIONALE AND
OBJECTIVES: Assessment of the breast tissue pattern asymmetry depicted on bilateral mammograms is routinely used by radiologists when reading and interpreting mammograms. The purpose of this study is to develop an automated scheme to detect breast tissue asymmetry depicted on bilateral mammograms and use the computed asymmetric features to predict the likelihood (or the risk) of women having or developing breast abnormalities or cancer.
MATERIALS AND METHODS: A testing dataset was selected from a large and diverse full-field digital mammography image database, which includes 100 randomly selected negative cases (not recalled during the screening) and 100 positive cases for having or developing breast abnormalities or cancer. Among these positive cases 40 were recalled (biopsy) because of suspicious findings in which 8 were determined as high risk with the lesions surgically removed and the remaining were proven to be benign, and 60 cases were acquired from examinations that were interpreted as negative (without dominant masses or microcalcifications) but the cancers were detected 6-18 months later. A computerized scheme was developed to detect asymmetry of mammographic tissue density represented by the related feature differences computed from bilateral images. Initially, each of 20 features was tested to classify between the positive and the negative cases. To further improve the classification performance, a genetic algorithm (GA) was applied to select a set of optimal features and build an artificial neural network (ANN). The leave-one-case-out validation method was used to evaluate the ANN classification performance.
RESULTS: Using a single feature, the maximum classification performance level measured by the area under the receiver operating characteristic curve (AUC) was 0.681 ± 0.038. Using the GA-optimized ANN, the classification performance level increased to an AUC = 0.754 ± 0.024. At 90% specificity, the ANN classifier yielded 42% sensitivity, in which 42 positive cases were correctly identified. Among them, 30 were the "prior" examinations of the cancer cases and 12 were recalled benign cases, which represent 50% and 30% sensitivity levels in these two subgroups, respectively.
CONCLUSIONS: This study demonstrated that using the computerized detected feature differences related to the bilateral mammographic breast tissue asymmetry, an automated scheme is able to classify a set of testing cases into the two groups of positive or negative of having or developing breast abnormalities or cancer. Hence, further development and optimization of this automated method may eventually help radiologists identify a fraction of women at high risk of developing breast cancer and ultimately detect cancer at an early stage.