OBJECTIVE:The purpose of this article is to evaluate the impact on the diagnosis of breast cancer of implementing full-field digital mammography (FFDM) in a multidisciplinary breast pathology unit and, 1 year later, the addition of a computer-aided detection (CAD) system.MATERIALS AND METHODS:A total of 13,453 mammograms performed between January and July of the years 2004, 2006, and 2007 were retrospectively reviewed using conventional mammography, digital mammography, and digital mammography plus CAD techniques. Mammograms were classified into two subsets: screening and diagnosis. Variables analyzed included cancer detection rate, rate of in situ carcinoma, tumor size at detection, biopsy rate, and positive predictive value of biopsy.RESULTS:FFDM increased the cancer detection rate, albeit not statistically significantly. The detection rate of in situ carcinoma increased significantly using FFDM plus CAD compared with conventional technique (36.8% vs 6.7%; p = 0.05 without Bonferroni statistical correction) for the screening dataset. Relative to conventional mammography, tumor size at detection decreased with digital mammography (T1, 61.5% vs 88%; p = 0.018) and with digital mammography plus CAD (T1, 79.7%; p = 0.03 without Bonferroni statistical correction). Biopsy rates in the general population increased significantly using CAD (10.6/1000 for conventional mammography, 14.7/1000 for digital mammography, and 17.9/1000 for digital mammography plus CAD; p = 0.02). The positive predictive value of biopsy decreased slightly, but not significantly, for both subsets.CONCLUSION:The incorporation of new techniques has improved the performance of the breast unit by increasing the overall detection rates and earlier detection (smaller tumors), both leading to an increase in interventionism.

Breast cancer continues to be the leading cause of death among women nowadays all over the world. Most frequent type of breast cancer is ductal carcinoma in situ (DCIS) and most frequent symptoms of DCIS recognized by mammography are clusters of Microcalcifications. In this paper, Resilient Backpropagation training algorithm is investigated for automated classification of clustered Microcalcifications (MCCs) as benign or malignant. The classifier is a part of computer aided disease diagnosis (CAD) system that is widely used to aid radiologists in the interpretation of mammograms. The performance of Resilient Backpropagation training algorithm is compared with a well known Batch Gradient Descent training algorithm. Such methods are explored not only for accuracy point of view but also for computational efficiency for MCCs characterization in mammograms. As input, these methods used mammogram features extracted from MCCs. Such methods are tested using images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). Receiver operating characteristic (ROC) analysis is used to evaluate and compare classification performance of these methods. Experimental results demonstrate that Resilient Backpropagation training algorithm could greatly reduce the computational complexity of Multi layer Feed Forward Backpropagation Artificial Neural Network (MLFFBP-ANN) while maintaining its best classification accuracy. It can produce lower false positives and false negatives than Batch Gradient Descent training algorithm.

The purpose of this study was to evaluate the effect of independent reading with computer-aided diagnosis (CAD) and independent double reading on radiologists' performance to characterize mass lesions on serial mammograms. Six radiologists rated 198 cases, 99 benign and 99 malignant. For each case, the mammograms from two consecutive screening rounds were available. The mass was visible on the prior view in 40% of the cases. Independently, a CAD programe also rated each mass lesion making use of information from prior and current views. The following reading situations were compared: single reading, independent reading with CAD, and independent double reading. Independent reading with CAD was implemented by averaging the scaled ratings from each radiologist and the scaled CAD scores. We implemented independent double reading by averaging the scaled scores from two radiologists. Results were evaluated using receiver-operating characteristic (ROC) methodology and multiple reader multiple case analysis. The average performance, measured as the area under the ROC curve (A(z) value), was 0.80 for the single-reading mode. For independent double reading, the average performance improved to 0.81. This improvement was not significant. For independent interpretation with CAD, the average performance significantly increased to 0.83 (P < 0.05). We conclude that CAD technology with temporal analysis has the potential to help radiologists with the task of discriminating between benign and malignant masses.

In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average Az value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.

In mammography, computer-aided diagnosis (CAD) techniques for mass detection and classification mainly use local image information to determine whether a region is abnormal or not. There is a lot of interest in developing CAD methods that use context, asymmetry, and multiple view information. However, it is not clear to what extent this may improve CAD results. In this study, we made use of human observers to investigate the potential benefit of using context information for CAD. We investigated to what extent human readers make use of context information derived from the whole breast area and from asymmetry for the tasks of mass detection and classification. Results showed that context information can be used to improve CAD programs for mass detection. However, there is still a lot to be gained from improvement of local feature extraction and classification. This is demonstrated by the fact that the observers did much better in classifying true positive (TP) and false positive (FP) regions than the CAD program. For classification of benign and malignant masses context seems to be less important.

Mammographic screening is widely used for early detection of breast cancer. Despite the success of screening programs, negative effects should not be underestimated. In many countries, only 15

A previously developed computerized scheme to detect masses has been further revised and several improvements were intended. Mammograms were digitized at a higher resolution with a mammographic laser scanner providing 12 bits. Some steps of the scheme, based on bilateral subtraction technique, were modified. Several new features were designed and a BPN neural network was used to reduce the number of false positives. Results obtained with the training set were encouraging, yielding a sensitivity of 85% and 1.54 mean number of false positives per image before applying false positive reduction. After applying false positive reduction, a sensitivity of 78.3% at a mean number of 0.4 false positives per image was obtained. The area under the AFROC curve was A1 = 0.808.