Mutual information is a popular intensity-based image similarity measure mainly used in image registration. This measure has been also very successful as the similarity metric in our knowledge-based computer-assisted detection (CADe) system for the detection of masses in screening mammograms. Our CADe system is designed to assess a new, query case based on its similarity with known cases stored in the knowledge database. However, intensity-based mutual information captures only relationships between the gray level values of corresponding pixels. This study presents a novel advancement of our CADe system by incorporating neighborhood textural information when estimating the mutual information of two images. Specifically, an entropy filter is applied to the images, effectively replacing each image pixel value with its neighborhood entropy. This pixel-based entropy is a localized measure of image texture. Then, the information-theoretic CAD system is asked to make a decision regarding the query case using the texture-based mutual information similarity metric. The entropy-based image enhancement and MI-based decision making processes are repeated at different neighborhood scales. Finally, an artificial network merges intensity-based and texture-based decisions to investigate possible improvements in mass detection performance. Given a database of 1,820 regions of interest (ROIs) extracted from screening mammograms (901 depicting a biopsy-proven mass and 919 depicting normal parenchyma) and a leave-one out sampling scheme, the study showed that our CADe system achieves an ROC area of 0.87±0.01 using the intensity-based ROC. The ROC performance for the texture-based CADe system ranges from 0.69±0.01 to 0.83±0.01 depending on the scale of analysis. The synergistic approach of the ANN using both intensity-based and texture-based information resulted in statistically significantly better performance with an ROC area index of 0.93±0.01.
Potential malignancy of a mammographic lesion can be assessed using the mathematically optimal likelihood ratio (LR) from signal detection theory. We developed a LR classifier for prediction of breast biopsy outcome of mammographic masses from BI-RADS findings. We used cases from Duke University Medical Center (645 total, 232 malignant) and University of Pennsylvania (496, 200). The LR was trained and tested alternatively on both subsets. Leave-one-out sampling was used when training and testing was performed on the same data set. When tested on the Duke set, the LR achieved a Received Operating Characteristic (ROC) area of 0.91± 0.01, regardless of whether Duke or Pennsylvania set was used for training. The LR achieved a ROC area of 0.85± 0.02 for the Pennsylvania set, again regardless of which set was used for training. When using actual case data for training, the LR's procedure is equivalent to case-based reasoning, and can explain the classifier's decisions in terms of similarity to other cases. These preliminary results suggest that the LR is a robust classifier for prediction of biopsy outcome using biopsy cases from different medical centers.
We investigated how the subdivision of breast biopsy cases into masses and calcifications influences breast cancer prediction for a case-based reasoning (CBR) classifier system. Mammographers' BI-RADS (TM) descriptions of mammographic lesions were used as input to predict breast biopsy outcome. The CBR classifier compared the case to be examined to a reference collection of cases and identified similar cases. The decision variable for each case was formed as the ratio of malignant similar cases to all similar cases. The reference data collection consisted of 1433 biopsy-proven mammography cases, and was divided into 3 categories: mass cases, calcification cases, and other. Performance was evaluated using ROC analysis and Round Robin sampling, and variance was estimated using a bootstrap analysis. The best ROC area for masses was 0.92+/- 0.01. At 98% sensitivity, about 209 (51%) patients with benign mass lesions might have been spared biopsy, while missing 5 (2%) malignancies. The best ROC area for calcifications was only 0.64+/- 0.02. At 98% sensitivity, 50 (12%) benign calcification cases could have been spared, while missing 5 (2%) malignancies. The CBR system performed substantially better on the masses than on the calcifications.
This paper investigates the effects of using different similarity measures for a case-based reasoning (CBR) classifier to predict breast cancer. The CBR classifier used a mammographer's BI-RADSTM description of a lesion to predict breast biopsy outcome. The classifier compared the case to be examined to a reference collection of cases and identified those that were similar. The decision variable was formed as the ratio of similar cases that were malignant to all similar cases. A reference collection of 1027 biopsy-proven cases from Duke University Medical Center was used as input. Both Euclidean and Hamming distance measures were compared using all possible combinations of nine BI-RADSTM features and age. Performance was evaluated using jackknife sampling and ROC analysis. For all combinations of features, it was found that Euclidean distance measure produced greater ROC areas and partial ROC areas than Hamming. The differences were significant at an alpha level of 0.05. The greatest ROC area of 0.82 +/- 0.01 was generated using six of the features and Euclidean distance measure. The results of both distance measures yielded greater ROC areas than previously reported values and were similar to results generated with an Artificial Neural Network using 10 features.
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