OBJECTIVES: A computer-aided diagnosis (CAD) scheme for determining histological classifications of breast masses is expected to be useful for clinicians in making a differential diagnosis. The purpose of this study was to evaluate the usefulness of using the CAD scheme on ultrasonographic images. METHODS: The database consisted of 390 breast ultrasonographic images with masses. Three experienced clinicians independently provided subjective ratings on the likelihood of malignancy for each of the 390 masses. Fifty benign masses (25 cysts and 25 fibroadenomas) and 50 malignant masses (25 noninvasive ductal carcinomas and 25 invasive ductal carcinomas) were selected as unknown cases for an observer study based on a stratified randomization method with the ratings. The likelihood of the histological classification in each unknown case was evaluated by the CAD scheme with image features that clinicians commonly use for describing masses. In the observer study, seven observers provided their confidence levels regarding the malignancy of the unknown case before and after viewing the likelihood of the histological classification. The usefulness of the CAD scheme was evaluated with a multireader multicase receiver operating characteristic (ROC) analysis. RESULTS: The areas under the ROC curves (AUCs) for all observers were improved by use of the CAD scheme. The average AUC increased from 0.716 without to 0.864 with the CAD scheme (P = .006). CONCLUSION: The presentation of the likelihood of the histological classification evaluated by the CAD scheme improved the clinicians' performance and therefore would be useful in making a differential diagnosis of masses on ultrasonographic images.
OBJECTIVES: A computer-aided diagnosis (CAD) scheme for determining histological classifications of breast masses is expected to be useful for clinicians in making a differential diagnosis. The purpose of this study was to evaluate the usefulness of using the CAD scheme on ultrasonographic images. METHODS: The database consisted of 390 breast ultrasonographic images with masses. Three experienced clinicians independently provided subjective ratings on the likelihood of malignancy for each of the 390 masses. Fifty benign masses (25 cysts and 25 fibroadenomas) and 50 malignant masses (25 noninvasive ductal carcinomas and 25 invasive ductal carcinomas) were selected as unknown cases for an observer study based on a stratified randomization method with the ratings. The likelihood of the histological classification in each unknown case was evaluated by the CAD scheme with image features that clinicians commonly use for describing masses. In the observer study, seven observers provided their confidence levels regarding the malignancy of the unknown case before and after viewing the likelihood of the histological classification. The usefulness of the CAD scheme was evaluated with a multireader multicase receiver operating characteristic (ROC) analysis. RESULTS: The areas under the ROC curves (AUCs) for all observers were improved by use of the CAD scheme. The average AUC increased from 0.716 without to 0.864 with the CAD scheme (P = .006). CONCLUSION: The presentation of the likelihood of the histological classification evaluated by the CAD scheme improved the clinicians' performance and therefore would be useful in making a differential diagnosis of masses on ultrasonographic images.