Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound
The purpose of this research was to demonstrate the feasibility of a computerized auto-assessment method in which a computer-aided diagnosis (CADx) system itself provides a level of confidence for its estimate for the probability of malignancy for each radiologist-identified lesion. The computer performance was assessed within a leave-one-case-out protocol using a database of sonographic images from 542 patients (19% cancer prevalence). We investigated the potential of computer-derived confidence levels both as 1) an output aid to radiologists and 2) as an automated method to improve the computer classification performance-in the task of differentiating between cancerous and benign lesions for the entire database. For the former, the CADx classification performance was assessed within ranges of confidence levels. For the latter, the computer-derived confidence levels were used in the determination of the computer-estimated probability of malignancy for each actual lesion based on probabilities obtained from different views. The use of this auto-assessment method resulted in the modest but statistically significant increase in the area under the receiver operating characteristic (ROC) curve (AUC value) of 0.01 with respect to the performance obtained using the "traditional" CADx approach, increasing the AUC value from 0.89 to 0.90 (p-value 0.03). We believe that computer-provided confidence levels may be helpful to radiologists who are using CADx output in diagnostic image interpretation as well as for automated improvement of the CADx classification for cancer.
ultrasound breast cancer computer-aided diagnosis (cad) lesion segmentation