This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of craniocaudal (CC) and mediolateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781±0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6 to 18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry.

We investigated the feasibility of converting a computer-aided detection (CAD) scheme for digitized screen-film mammograms to full-field digital mammograms (FFDM) and assessing CAD performance on a large database that included 6478 FFDM images acquired on 1120 women with 525 cancer and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) “high-risk” recommended for surgical excision, (4) recalled but negative, and (5) screening negative (not-recalled). A previously developed CAD scheme for masses depicted on digitized images was converted and re-optimized for FFDM images while keeping the same image processing structure. CAD performance was analyzed on the entire database. The case-based CAD sensitivity was 75.6% (397/525) for the “current” mammograms and 40.8% (42/103) for the “prior” mammograms deemed negative during clinical interpretation but “visible” during retrospective review. The region-based CAD sensitivity was 58.1% (618/1064) for the “current” mammograms and 28.4% (57/201) for the “prior” mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the “current” and the “prior” examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image ranging from 0.29 to 0.51 for the five case groups. This study suggests that (1) digitized image based CAD can be converted for FFDM while performing at a comparable, or better, level, (2) CAD detects a substantial fraction of cancers depicted on “prior” examinations, albeit most were marked only on one view, and (3) CAD tends to mark more false-positives on “difficult” negative cases that are more visually difficult for radiologists to interpret.

This study aims to develop a new computer-aided detection (CAD) scheme to detect early interstitial lung disease (ILD) using low-dose computed tomography (CT) examinations. The CAD scheme classifies each pixel depicted on the segmented lung areas into positive or negative groups for ILD using a mesh-grid-based region growth method and a multi-feature-based artificial neural network (ANN). A genetic algorithm was applied to select optimal image features and the ANN structure. In testing each CT examination, only pixels selected by the mesh-grid region growth method were analyzed and classified by the ANN to improve computational efficiency. All unselected pixels were classified as negative for ILD. After classifying all pixels into the positive and negative groups, CAD computed a detection score based on the ratio of the number of positive pixels to all pixels in the segmented lung areas, which indicates the likelihood of the test case being positive for ILD. When applying to an independent testing dataset of 15 positive and 15 negative cases, the CAD scheme yielded the area under receiver operating characteristic curve (AUC = 0.884 ± 0.064) and 80.0% sensitivity at 85.7% specificity. The results demonstrated the feasibility of applying the CAD scheme to automatically detect early ILD using low-dose CT examinations.

This study aims to improve breast cancer risk stratification. A seven-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system was designed, assembled, and utilized to establish a data set of examinations from 174 women. Three classifiers, including artificial neural network (ANN), support vector machine (SVM), and Gaussian mixture model (GMM), were independently developed to predict the likelihood of each woman to be recommended for biopsy. The performances of these classifiers were compared, and seven fusion methods for integrating these classifiers were investigated. The results showed that among the three classifiers, the ANN yielded the highest performance with an area under the curve (AUC) of 0.81 for the receiver operating characteristic (ROC), while SVM and GMM achieved AUCs of 0.80 and 0.78, respectively. Improvements of up to 3% were obtained using fusion of the three classifiers, with the largest improvement obtained using either a "minimum score" rule or a "weighted sum" rule. Comparing different combinations of two out of the three classifiers, the weighted sum rule provided the most robust and consistent results, with AUCs of 0.81, 0.83, and 0.82 for the different combinations of ANN and SVM, ANN and GMM, and SVM and GMM, respectively. Furthermore, at 90% specificity, the ANN, the weighted sum- and min rule-based classifiers, all detected 67% of the verified cancer cases as compared with 50, 50, and 60% detection of the high risk cases, respectively. The study demonstrated that REIS examinations provide relevant information for developing breast cancer risk stratification tools and that using fusion of several not-fully-correlated classifiers can improve classification performance.

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