• Fatemeh Zabihollahy, Eranga Ukwatta, Satheesh Krishna, and Nicola Schieda.
• Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada.
• J Magn Reson Imaging. 2020 Apr 1; 51 (4): 1223-1234.

BackgroundAccurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful.Purpose/HypothesisTo describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U-Net-based model.Study TypeRetrospective, case-control.PopulationIn all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively.Field Strength3T, ADC maps.SequenceADC map.AssessmentThe ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI-radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per-slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t-test was used to test the differences between the performances of constituent networks of the ensemble model.ResultsOur developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779.Data ConclusionAn ensemble U-Net-based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images.Level Of Evidence4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223-1234.© 2019 International Society for Magnetic Resonance in Medicine.

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