AutoProstate: towards automated reporting of prostate MRI for prostate cancer assessment using deep learning

Title: AutoProstate: towards automated reporting of prostate MRI for prostate cancer assessment using deep learning
Authors: Mehta, P
Antonelli, M
Singh, S
Grondecka, N
Johnston, EW
Ahmed, HU
Emberton, M
Punwani, S
Ourselin, S
Item Type: Journal Article
Abstract: Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.
Issue Date: 6-Dec-2021
Date of Acceptance: 3-Dec-2021
URI: http://hdl.handle.net/10044/1/98236
DOI: 10.3390/cancers13236138
ISSN: 2072-6694
Publisher: MDPI AG
Start Page: 1
End Page: 21
Journal / Book Title: Cancers
Volume: 13
Issue: 23
Copyright Statement: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Wellcome Trust
Funder's Grant Number: 204998/Z/16/Z
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
automatic report
computer-aided diagnosis
convolutional neural network
deep learning
lesion detection
lesion classification
magnetic resonance imaging
prostate cancer
segmentation
COMPUTER-AIDED DETECTION
MULTIPARAMETRIC MRI
DIAGNOSTIC-ACCURACY
SEGMENTATION
automatic report
computer-aided diagnosis
convolutional neural network
deep learning
lesion classification
lesion detection
magnetic resonance imaging
prostate cancer
segmentation
Science & Technology
Life Sciences & Biomedicine
Oncology
automatic report
computer-aided diagnosis
convolutional neural network
deep learning
lesion detection
lesion classification
magnetic resonance imaging
prostate cancer
segmentation
COMPUTER-AIDED DETECTION
MULTIPARAMETRIC MRI
DIAGNOSTIC-ACCURACY
SEGMENTATION
1112 Oncology and Carcinogenesis
Publication Status: Published
Article Number: ARTN 6138
Online Publication Date: 2021-12-06
Appears in Collections:Department of Surgery and Cancer



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