The purpose of this study was to develop a quantitative method for detection and segmentation of clinically significant (ISUP grade ≥ 2) prostate cancer (PCa) in low-risk patient. A consecutive cohort of 356 patients (active surveillance) was selected and divided in two groups: 1) MRI and targeted-biopsy positive PCa, 2) MRI and standard-biopsy negative PCa. A 3D convolutional neural network was trained in three-fold cross validation with MRI and targeted-biopsy positive patient's data using two mp-MRI sequences (T2-weighted, DWI-b800) and ADC map as input. After training, the model was tested on separate positive and negative patients to evaluate the performance. The model achieved an average area under the curve (AUC) of the receiver operating characteristics is 0.78 (sensitivity = 85%, specificity = 72%). The diagnostic performance of the proposed method in segmenting significant PCa and to conform non-significant PCa in low-risk patients is characterized by a good AUC and negative-predictive-value.

Active Surveillance, Convolutional Neural Network, Multiparametric MRI, Prostate Cancer, Segmentation,
17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
no subscription
Erasmus MC: University Medical Center Rotterdam

Arif, M. (Muhammad), Schoots, I.G, Castillo, J.M.T. (Jose M. T.), Roobol-Bouts, M.J, Niessen, W.J, & Veenland, J.F. (2020). Computer Aided Diagnosis of Clinically Significant Prostate Cancer in Low-Risk Patients on Multi-Parametric MR Images Using Deep Learning. In Proceedings - International Symposium on Biomedical Imaging (pp. 1482–1485). doi:10.1109/ISBI45749.2020.9098577