Computer-aided classification of BI-RADS category 3 breast lesions.

PURPOSE: To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation. MATERIALS AND METHODS: A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least two of four radiologists. The CAC system automatically extracted from the digitized mammograms quantitative features that characterized the lesions. The system then used a classification scheme to score the lesions by the likelihood of their malignancy on the basis of these features. The classification scheme was trained with 646 pathologically proved cases (323 malignant), and the results were tested with receiver operating characteristic (ROC) analysis by using the jackknife method. Sensitivity, specificity, positive predictive value, and accuracy were calculated. Category 3 lesions were stratified among BI-RADS categories 2-5 according to CAC-assigned lesion score, and this classification was compared with the results of pathologic analysis. RESULTS: Jackknife analysis of CAC results in the training data set yielded a sensitivity of 94%, specificity of 78%, positive predictive value of 81%, and area under the ROC curve of 0.90. Of the 42 malignant lesions that had been classified at conventional interpretation as probably benign, nine were assigned by the CAC system to BI-RADS category 4, and 29 were assigned to category 5. The CAC system correctly upgraded the BI-RADS classification of these 38 lesions (sensitivity, 90%) and incorrectly upgraded the classification of only 20 benign lesions (specificity, 69%). CONCLUSION: The CAC system scored 38 of the 42 malignant lesions initially assigned to BI-RADS category 3 as BI-RADS category 4 or 5, and thus correctly upgraded the category in 90% of these lesions.

Stratification of mammographic computerized analysis by BI-RADS categories.

The Breast Imaging Reporting and Data System (BI-RADS) was implemented to standardize characterization of mammographic findings. The purpose of the present study was to evaluate in which BI-RADS categories the changes recommended by computerized mammographic analysis are most beneficial. Archival cases including, 170 masses (101 malignant, 69 benign) and 63 clusters of microcalcifications (MCs; 36 malignant, 27 benign), were evaluated retrospectively, using the BI-RADS categories, by several radiologists, blinded to the pathology results. A computerized system then automatically extracted from the digitized mammogram features characterizing mammographic lesions, which were used to classify the lesions. The results of the computerized classification scheme were compared, by receiver operating characteristics (ROC) analysis, to the conventional interpretation. In the "low probability of malignancy group" (excluding BI-RADS categories 4 and 5), computerized analysis improved the A(z )of the ROC curve significantly, from 0.57 to 0.89. In the "high probability of malignancy group" (mostly category 5) the computerized analysis yielded an ROC curve with an A(z )of 0.99. In the "intermediate probability of malignancy group" computerized analysis improved the A(z )significantly, from 0.66 for to 0.83. Pair-wise analysis showed that in the latter group the modifications resulting from computerized analysis were correct in 83% of cases. Computerized analysis has the ability to improve the performance of the radiologists exactly in the BI-RADS categories with the greatest difficulties in arriving at a correct diagnosis. It increased the performance significantly in the problematic group of "intermediate probability of malignancy" and pinpointed all the cases with missed cancers in the "low probability" group.

Can the size of microcalcifications predict malignancy of clusters at mammography?

RATIONALE AND OBJECTIVES: The purpose of this study was to determine whether the size of mammographically detected microcalcifications is predictive of malignancy. MATERIALS AND METHODS: Two hundred sixty mammograms showing clustered microcalcifications with proven diagnoses (160 malignant, 100 benign) were respectively reviewed by experienced mammographers. Lesions that were obviously benign in appearance were excluded from the study. A computer-aided diagnosis system digitized the lesions at 600 dpi, and the microcalcifications on the digital image were interactively defined by mammographers. Subsequently, three quantitative features that reflected the size of the microcalcifications-length, area, and brightness-were automatically extracted by the system. For each feature, the standard average of values obtained for individual calcifications within the cluster and the average with emphasis on extreme values (E) obtained in a single cluster were analyzed and matched with pathologic results. RESULTS: In the malignant group of cases, the mean values of the standard average length and area were significantly higher (P < .0001) than the mean values in the benign group. Distribution analysis demonstrated that an average length of more than 0.41 mm was associated with malignant lesions 77% of the time, while an average length of less than 0.41 mm was associated with benign lesions 71% of the time. The mean of the average length (E) and area (E) of microcalcifications within the cluster demonstrated an even higher discriminative power when compared with the standard average length and area. The average brightness, on the other hand, showed only a low discriminative power. CONCLUSION: Digital computerized analysis of mammographically detected calcifications demonstrated that the average length and area of the calcifications in benign clusters were significantly smaller than those in malignant clusters.