Evaluation of a wavelet-based computer-assisted detection system for identifying microcalcifications in digital full-field mammography.

Abstract

PURPOSE: To evaluate a new wavelet-based computer-assisted detection (CAD) system for detecting and enhancing microcalcifications. MATERIAL AND METHODS: A total of 280 mammograms acquired by full-field digital mammography (Senographe 2000D; G.E. Medical Systems Milwaukee, Wisc., USA) were analyzed with and without a new wavelet-based CAD system for detecting and enhancing microcalcifications. The mammograms comprised roughly equal numbers of cases from each of the BIRADS (Breast Imaging, Reporting and Data System, according to the American College of Roentgenology) categories 1-5. Histologic confirmation was available for all of the 180 cases assigned BIRADS categories 3-5. Four readers interpreted all 280 images for suspicious microcalcifications using a scale of 1-5. The readers alternately assessed 5 images with and 5 without CAD. In a second reading immediately following the first, the readers had to reassess the 280 mammograms. The images that had already been interpreted without CAD were now presented with CAD and vice versa. The images were interpreted as soft copies on a diagnostic mammography workstation (Image Diagnost GmbH, Neufahrn/Munich, Germany). All images interpreted with CAD were presented with enhancement of microcalcifications by wavelet algorithms and prompting of microcalcifications. ROC (receiver operating characteristic) analyses were performed, and image interpretation time with and without CAD was measured. RESULTS: The overall time for interpretation required by all 4 readers together was 483 min with CAD compared to 580 min without CAD. ROC analysis revealed no significant advantage of CAD for the individual readers. Readers 3 (0.811/0.817) and 4 (0.799/0.843) had a slightly improved AUC (area under the curve) with CAD. Readers 1 and 2 had a slightly lower AUC with CAD (0.832 versus 0.861 and 0.818 versus 0.849). CONCLUSION: The CAD system significantly (P<0.05, t test) speeded up image interpretation with respect to the identification of microcalcifications, while the diagnostic quality remained almost identical under the study conditions.