Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI

Pritesh Mehta; Michela Antonelli; Hashim Ahmed; Mark Emberton; Shonit Punwani; Sebastien Ourselin

doi:10.1117/12.2547682

16 March 2020 Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI

Pritesh Mehta, Michela Antonelli, Hashim Ahmed, Mark Emberton, Shonit Punwani, Sebastien Ourselin

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131433 (2020) https://doi.org/10.1117/12.2547682
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

In this work, we present a computer-aided diagnosis system that uses deep learning and decision fusion to classify patients into one of three classes: “Likely Prostate Cancer," “Equivocal" and “Likely not Prostate Cancer." We impose the group “Equivocal" to reduce misclassifications by allowing for uncertainty, akin to prostate imaging reporting systems used by radiologists. We trained 3D convolutional neural networks to perform two binary patient-level classification tasks: classification of patients with/without prostate cancer and classification of patients with/without clinically significant prostate cancer. Networks were trained separately using volumetric T2-weighted images and apparent diffusion coefficient maps for both tasks. The probabilistic outputs of the resulting four trained networks were combined using majority voting followed by the max operator to classify patients into one of the three classes mentioned above. All networks were trained using patient-level labels only, which is a key advantage of our system since voxel-level tumour annotation is often unavailable due to the time and effort required of a radiologist. Our system was evaluated by retrospective analysis on a previously collected trial dataset. At a higher sensitivity setting, our system achieved 0.97 sensitivity and 0.31 specificity compared to an experienced radiologist who achieved 0.99 sensitivity and 0.12 specificity. At a lower sensitivity setting, our system achieved 0.78 sensitivity and 0.77 specificity compared to 0.76 sensitivity and 0.77 specificity for the experienced radiologist. We envision our system acting as a second reader in pre-biopsy screening applications.

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Pritesh Mehta, Michela Antonelli, Hashim Ahmed, Mark Emberton, Shonit Punwani, and Sebastien Ourselin "Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131433 (16 March 2020); https://doi.org/10.1117/12.2547682

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KEYWORDS

Cancer

Imaging systems

Prostate

CAD systems

Magnetic resonance imaging

Binary data

Prostate cancer

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