Rule-based data-driven approach for computer aided diagnosis of the peripheral zone prostate cancer from multiparametric MRI: Proof of concept

In this paper, we present a new unsupervised prostate cancer (PCa) localization algorithm for the peripheral zone (PZ), utilizing well-established rules used in clinical PCa diagnosis from mpMRI data. We perform clustering on ADC and DWI images accompanied by T2W examination of clustered regions and then combined with DCE findings. For each of the 10 analysed patients, we obtain a likelihood map showing suspicious areas. We evaluate our method by comparison against radiological MR tumor segmentations and delineations in histopathological whole-mount sections automatically registered to the MR, using voxel-wise ROC analysis. The resulting mean AUC values for our algorithm were 0.81 and 0.67 with radiological and histopathological ground truth, respectively, while the mean AUC for the radiological segmentation with the histopathological segmentation as the ground truth was 0.60. We conclude that the proposed approach can localize PZ PCa with good accuracy and could be used as an aid for radiologists.