Incidental detection of prostate cancer with computed tomography scans

Abstract Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques are either invasive, resource intensive, or has l...

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Autores principales:	Steven Korevaar, Ruwan Tennakoon, Mark Page, Peter Brotchie, John Thangarajah, Cosmin Florescu, Tom Sutherland, Ning Mao Kam, Alireza Bab-Hadiashar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/694c313d6c154e93b4d8962f798f5592
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spelling	oai:doaj.org-article:694c313d6c154e93b4d8962f798f55922021-12-02T15:51:14ZIncidental detection of prostate cancer with computed tomography scans10.1038/s41598-021-86972-y2045-2322https://doaj.org/article/694c313d6c154e93b4d8962f798f55922021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86972-yhttps://doaj.org/toc/2045-2322Abstract Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques are either invasive, resource intensive, or has low efficacy, making widespread early detection onerous. Inspired by the recent success of deep convolutional neural networks (CNN) in computer aided detection (CADe), we propose a new CNN based framework for incidental detection of clinically significant prostate cancer (csPCa) in patients who had a CT scan of the abdomen/pelvis for other reasons. While CT is generally considered insufficient to diagnose PCa due to its inferior soft tissue characterisation, our evaluations on a relatively large dataset consisting of 139 clinically significant PCa patients and 432 controls show that the proposed deep neural network pipeline can detect csPCa patients at a level that is suitable for incidental detection. The proposed pipeline achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.88 (95% Confidence Interval: 0.86–0.90) at patient level csPCa detection on CT, significantly higher than the AUCs achieved by two radiologists (0.61 and 0.70) on the same task.Steven KorevaarRuwan TennakoonMark PagePeter BrotchieJohn ThangarajahCosmin FlorescuTom SutherlandNing Mao KamAlireza Bab-HadiasharNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Steven Korevaar Ruwan Tennakoon Mark Page Peter Brotchie John Thangarajah Cosmin Florescu Tom Sutherland Ning Mao Kam Alireza Bab-Hadiashar Incidental detection of prostate cancer with computed tomography scans
description	Abstract Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques are either invasive, resource intensive, or has low efficacy, making widespread early detection onerous. Inspired by the recent success of deep convolutional neural networks (CNN) in computer aided detection (CADe), we propose a new CNN based framework for incidental detection of clinically significant prostate cancer (csPCa) in patients who had a CT scan of the abdomen/pelvis for other reasons. While CT is generally considered insufficient to diagnose PCa due to its inferior soft tissue characterisation, our evaluations on a relatively large dataset consisting of 139 clinically significant PCa patients and 432 controls show that the proposed deep neural network pipeline can detect csPCa patients at a level that is suitable for incidental detection. The proposed pipeline achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.88 (95% Confidence Interval: 0.86–0.90) at patient level csPCa detection on CT, significantly higher than the AUCs achieved by two radiologists (0.61 and 0.70) on the same task.
format	article
author	Steven Korevaar Ruwan Tennakoon Mark Page Peter Brotchie John Thangarajah Cosmin Florescu Tom Sutherland Ning Mao Kam Alireza Bab-Hadiashar
author_facet	Steven Korevaar Ruwan Tennakoon Mark Page Peter Brotchie John Thangarajah Cosmin Florescu Tom Sutherland Ning Mao Kam Alireza Bab-Hadiashar
author_sort	Steven Korevaar
title	Incidental detection of prostate cancer with computed tomography scans
title_short	Incidental detection of prostate cancer with computed tomography scans
title_full	Incidental detection of prostate cancer with computed tomography scans
title_fullStr	Incidental detection of prostate cancer with computed tomography scans
title_full_unstemmed	Incidental detection of prostate cancer with computed tomography scans
title_sort	incidental detection of prostate cancer with computed tomography scans
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/694c313d6c154e93b4d8962f798f5592
work_keys_str_mv	AT stevenkorevaar incidentaldetectionofprostatecancerwithcomputedtomographyscans AT ruwantennakoon incidentaldetectionofprostatecancerwithcomputedtomographyscans AT markpage incidentaldetectionofprostatecancerwithcomputedtomographyscans AT peterbrotchie incidentaldetectionofprostatecancerwithcomputedtomographyscans AT johnthangarajah incidentaldetectionofprostatecancerwithcomputedtomographyscans AT cosminflorescu incidentaldetectionofprostatecancerwithcomputedtomographyscans AT tomsutherland incidentaldetectionofprostatecancerwithcomputedtomographyscans AT ningmaokam incidentaldetectionofprostatecancerwithcomputedtomographyscans AT alirezababhadiashar incidentaldetectionofprostatecancerwithcomputedtomographyscans
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Incidental detection of prostate cancer with computed tomography scans

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