Automated 24-sector grid-map algorithm for prostate mpMRI improves precision and efficacy of prostate lesion location...

The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance.

An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate. To evaluate the efficacy of the method, fiducials for random locations within the prostate were placed, and the corresponding sectors were determined for 50 mpMRI datasets. The reference standard was defined in a consensus read by two expert uroradiologists. Annotated fiducial locations were evaluated automatically by the ASG and by four radiologists in two reads with and without the help of a superimposed sector grid-map and the success rate was compared.

The ASG algorithm identified the correct prostate sector of the annotated lesions in 80 % (40/50 reads) of the cases and outperformed readings of the four radiologists with 55 % (109/200), p < 0.0001. The added use of the 24 ASG map significantly improved the rate of correct sector annotation for the four radiologists to 71 % (141/200), p < 0.004.

The 24 ASG map was effective for prostate sector segmentation and significantly improved location reporting of prostate lesions.