AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning
Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
School of Biomedical Engineering & Imaging Sciences, King’s College London, London SE1 7EH, UK
Centre for Medical Imaging, University College London, London WC1E 6BT, UK
Department of Medical Radiology, Medical University of Lublin, 20-059 Lublin, Poland
Interventional Radiology, Royal Marsden Hospital, London SW3 6JJ, UK
Imperial Prostate, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London WC1E 6BT, UK
Author to whom correspondence should be addressed.
Academic Editor: Fabio Zattoni
Received: 12 November 2021
Revised: 30 November 2021
Accepted: 3 December 2021
Published: 6 December 2021
International guidelines recommend multiparametric magnetic resonance imaging (mpMRI) of the prostate for use by radiologists to identify lesions containing clinically significant prostate cancer, prior to confirmatory biopsy. Automatic assessment of prostate mpMRI using artificial intelligence algorithms holds a currently unrealized potential to improve the diagnostic accuracy achievable by radiologists alone, improve the reporting consistency between radiologists, and enhance reporting quality. In this work, we introduce AutoProstate: a deep learning-powered framework for automatic MRI-based prostate cancer assessment. In particular, AutoProstate utilizes patient data and biparametric MRI to populate an automatic web-based report which includes segmentations of the whole prostate, prostatic zones, and candidate clinically significant prostate cancer lesions, and in addition, several derived characteristics with clinical value are presented. Notably, AutoProstate performed well in external validation using the PICTURE study dataset, suggesting value in prospective multicentre validation, with a view towards future deployment into the prostate cancer diagnostic pathway.