Multi-parametric MRI (mp-MRI) has shown to be useful in contemporary prostate biopsy procedures. Unfortunately, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a potential solution and have been shown to boost diagnostic accuracy. We measured the accuracy of a CAD model using a systematic sampling algorithm to remove any spatial bias present in our input. We trained four classifiers with 1–10 features chosen by forward feature selection for each and reported the system with the highest AUC in both the peripheral zone and central gland. Furthermore, we investigated the effect on system performance by varying the minimum tumour size threshold and by varying the average difference in area between malignant and healthy tissue samples. The CAD model was able to classify malignant vs. benign tissue with accuracies competitive with those reported in the literature. Eroding healthy tissue ROIs positively biased the system’s performance for the PZ, but no such trend was found in the CG. Once fully validated, this system has the potential to imp

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501D (13 March 2019); doi: 10.1117/12.2513301

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)