PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) workstation in classifying cancer in a realistic data set representative of a clinical diagnostic breast ultrasonography (US) practice. MATERIALS AND METHODS: The database consisted of consecutive diagnostic breast US scans collected with informed consent with a protocol approved by the institutional review board and compliant with the HIPAA. Images from 508 patients with a total of 1046 distinct abnormalities were used. One hundred one patients had breast cancer. Results both for patients in whom the lesion abnormality was proved with either biopsy or aspiration (n = 183) and for all patients irrespective of biopsy status (n = 508) are presented. The ability of the CAD workstation to help differentiate malignancies from benign lesions was evaluated with a leave-one-out-by-case analysis. The clinical specificity of the radiologists for this dataset was determined according to the biopsy rate and outcome. RESULTS: In the task of differentiating cancer from all other lesions sent to biopsy, the CAD workstation obtained an area under the receiver operating characteristic curve (AUC) value of 0.88, with 100% sensitivity at 26% specificity (157 cancers and 362 lesions total). The radiologists' specificity at 100% sensitivity for this set was zero. When analyzing all lesions irrespective of biopsy status, which is more representative of actual clinical practice, the CAD scheme obtained an AUC of 0.90 and 100% sensitivity at 30% specificity (157 cancers and 1046 lesions total). The radiologists' specificity at 100% sensitivity for this set was 77%. CONCLUSION: Current levels of computer performance warrant a clinical evaluation of the potential of US CAD to aid radiologists in lesion work-up recommendations.
PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) workstation in classifying cancer in a realistic data set representative of a clinical diagnostic breast ultrasonography (US) practice. MATERIALS AND METHODS: The database consisted of consecutive diagnostic breast US scans collected with informed consent with a protocol approved by the institutional review board and compliant with the HIPAA. Images from 508 patients with a total of 1046 distinct abnormalities were used. One hundred one patients had breast cancer. Results both for patients in whom the lesion abnormality was proved with either biopsy or aspiration (n = 183) and for all patients irrespective of biopsy status (n = 508) are presented. The ability of the CAD workstation to help differentiate malignancies from benign lesions was evaluated with a leave-one-out-by-case analysis. The clinical specificity of the radiologists for this dataset was determined according to the biopsy rate and outcome. RESULTS: In the task of differentiating cancer from all other lesions sent to biopsy, the CAD workstation obtained an area under the receiver operating characteristic curve (AUC) value of 0.88, with 100% sensitivity at 26% specificity (157 cancers and 362 lesions total). The radiologists' specificity at 100% sensitivity for this set was zero. When analyzing all lesions irrespective of biopsy status, which is more representative of actual clinical practice, the CAD scheme obtained an AUC of 0.90 and 100% sensitivity at 30% specificity (157 cancers and 1046 lesions total). The radiologists' specificity at 100% sensitivity for this set was 77%. CONCLUSION: Current levels of computer performance warrant a clinical evaluation of the potential of US CAD to aid radiologists in lesion work-up recommendations.
Authors: Nicholas P Gruszauskas; Karen Drukker; Maryellen L Giger; Ruey-Feng Chang; Charlene A Sennett; Woo Kyung Moon; Lorenzo L Pesce Journal: Radiology Date: 2009-10-28 Impact factor: 11.105
Authors: Moi Hoon Yap; Manu Goyal; Fatima M Osman; Robert Martí; Erika Denton; Arne Juette; Reyer Zwiggelaar Journal: J Med Imaging (Bellingham) Date: 2018-10-10