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Robustness of Computerized Lesion Detection and Classification Scheme across Different Breast US Platforms¹

¹ From the Department of Radiology MC2026, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637. Received August 13, 2004; revision requested October 21; revision received December 17; accepted January 20, 2005. Supported in part by United States Public Health Service grant CA89452 and U.S. Army Medical Research and Materiel Command grant 97-2445. Address correspondence to K.D. (e-mail: kdrukker{at}uchicago.edu).

PURPOSE: To evaluate the performance of a computerized detection and diagnosis method with breast ultrasonographic (US) images obtained with US equipment from two different manufacturers.

MATERIALS AND METHODS: Two independent clinical breast US databases were used in this performance study. Data collection and database use were HIPAA-compliant and followed institutional review board–approved protocols, with waiver of informed consent. One database consisted of 1740 images obtained in 458 women with Philips US equipment. The other database consisted of 151 images obtained in 151 women with Siemens US equipment. The testing protocols included independent testing and round-robin analysis. The computerized scheme detects potential lesions, calculates imaging features for all candidate lesions, and subsequently classifies candidate lesions into different categories. Two separate classification tasks were evaluated: distinction between all actual lesions and false-positive detections and distinction between actual cancers and all other detected lesion candidates. Statistical analysis was performed by using both receiver operating characteristic (ROC) and free-response ROC methods.

RESULTS: For the distinction between all actual lesions and false-positive detections, area under the ROC curve (A_z) values ranged between 0.87 and 0.95 for different testing protocols. In two instances, the difference in performance between databases was significant (P < .01), but it was shown that this was due to the difference in size of the databases. In the distinction of cancer from all other detections, the A_z values ranged between 0.80 and 0.86. No statistically significant difference was found among the different testing protocols in this instance.

CONCLUSION: These results indicate that the performance of this fully automated computerized lesion detection and classification method, which demonstrated robustness over the different US equipment used, is promising.

© RSNA, 2005

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