Combining T2-weighted with dynamic MR images for computerized classification of prostate lesions

摘要

In this study, we investigate the diagnostic performance of our CAD system when discriminating prostate cancer from benign lesions and normal peripheral zone using registered multi-modal images. We have developed a method that automatically extracts quantitative T2 values out of acquired T2-w images and evaluated its additional value to the discriminating performance of our CAD system. This study addresses 2 issues when using both T2-w and dynamic MR images for the characterization of prostate lesions. Firstly, T2-w images do not provide quantitative values, and secondly, images can be misaligned due to patient movements. To compensate, a mutual information registration strategy is performed after which T2 values are estimated using the acquired proton density images. From the resulted quantitative T2 maps as well as the dynamic images relevant features were extracted for training a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. General performance of the scheme was evaluated using the area under the ROC curve. We conclude that it is feasible to automatically extract diagnostic T2 values out of acquired T2-w images. Furthermore, a discriminating performance of 0.75 (0.66-0.85) was obtained when only using T2-values as feature. Combining the T2 values with pharmacokinetic parameters did not increase diagnostic performance in a pilot study.