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Computerized Evaluation of Mammographic Lesions: What Diagnostic Role Does the Shape of the Individual Microcalcifications Play Compared with the Geometry of the Cluster?

¹ Department of Electro-Optics, Jerusalem College of Technology, Jerusalem, Israel.
² Department of Radiology, Hadassah University Hospital, Jerusalem, Israel.
³ Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY.
⁴ Staten Island University Hospital, 475 Seaview Ave., Staten Island, NY 10305.
⁵ Department of Electronics, Jerusalem College of Technology, Jerusalem, Israel.

OBJECTIVE. The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system.

MATERIALS AND METHODS. Three hundred twenty-four cases of clustered microcalcifications with biopsy-proven results were digitized at 42-µm resolution and analyzed on a computerized system. The shape factor and number of neighbors were computed for each microcalcification, and the eccentricity of the cluster was computed as well. The shape factor is related to the individual microcalcification; the average number of neighbors and the cluster eccentricity reflect the cluster geometry. Stepwise discriminant analysis was used to evaluate the contribution of the extracted features in predicting malignancy. The performance of a classifier based on the features selected by stepwise discriminant analysis was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS. To obtain the best discrimination model, we used stepwise discriminant analysis to select the average number of neighbors and the shape of the individual microcalcification, but excluded cluster eccentricity. A classification scheme assigned the average number of neighbors a weighting factor, which was 1.49 times greater than that assigned to the shape factor of the individual microcalcification. A scheme based only on these two features yielded an ROC curve with an area under the curve (A_z) of 0.87, indicating a positive predictive value of 61% for 98% sensitivity.

CONCLUSION. Computerized analysis permitted calculations reflecting the shape of individual microcalcification and the geometry of clusters of microcalcifications. For the computerized classification scheme studied, the cluster geometry was more effective in differentiating benign from malignant clusters than was the shape of individual microcalcification.

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