A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy
Objective To develop and externally validate a predictive model for detection of significant prostate cancer. Patients and Methods Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External val...
Gespeichert in:
| Veröffentlicht in: | BJU international 2017-12, Vol.120 (6), p.774-781 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 781 |
|---|---|
| container_issue | 6 |
| container_start_page | 774 |
| container_title | BJU international |
| container_volume | 120 |
| creator | Leeuwen, Pim J. Hayen, Andrew Thompson, James E. Moses, Daniel Shnier, Ron Böhm, Maret Abuodha, Magdaline Haynes, Anne‐Maree Ting, Francis Barentsz, Jelle Roobol, Monique Vass, Justin Rasiah, Krishan Delprado, Warick Stricker, Phillip D. |
| description | Objective
To develop and externally validate a predictive model for detection of significant prostate cancer.
Patients and Methods
Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate‐specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling.
Results
In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer.
Conclusions
Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over‐detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed. |
| doi_str_mv | 10.1111/bju.13814 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1869968355</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1967284223</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3884-d12a061affe9695a9eb05761727be7de2f323855ecf21b62d94d419fcd0d261a3</originalsourceid><addsrcrecordid>eNp1kbFuFDEQhi1EREKg4AWQJRooLrG9u167TCICiSLREInO8trjw8eufdheoeso8gA8I0-Cj00oIjGNxzPf_JrRj9ArSk5ojdNhM5_QRtD2CTqiLW9XLSVfnj7kRPJD9DznDSG1wLtn6JAJRnop6BG6O8PTPBa_1UlPUJI3eNLrAKUmCXIMOhjAvtZ8WP_--WvQGSxOPn_DU7Qw4hKxhQJp8gFw-QpLLzqc_Tp4540OBW9TzEUXwGYvl-rfx7QfHXzc5t0LdOD0mOHl_XuMbi_ff774uLr59OHq4uxmZRoh2pWlTBNOtXMguey0hIF0Pac96wfoLTDXsEZ0HRjH6MCZla1tqXTGEsvqXHOM3i66dZ_vM-SiJp8NjKMOEOesqOBSctF0XUXfPEI3cU6hbqeo5D0TLWNNpd4tlKkH5gRO1csmnXaKErW3RlVr1F9rKvv6XnEeJrD_yAcvKnC6AD_8CLv_K6nz69tF8g_cu5sN</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1967284223</pqid></control><display><type>article</type><title>A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Leeuwen, Pim J. ; Hayen, Andrew ; Thompson, James E. ; Moses, Daniel ; Shnier, Ron ; Böhm, Maret ; Abuodha, Magdaline ; Haynes, Anne‐Maree ; Ting, Francis ; Barentsz, Jelle ; Roobol, Monique ; Vass, Justin ; Rasiah, Krishan ; Delprado, Warick ; Stricker, Phillip D.</creator><creatorcontrib>Leeuwen, Pim J. ; Hayen, Andrew ; Thompson, James E. ; Moses, Daniel ; Shnier, Ron ; Böhm, Maret ; Abuodha, Magdaline ; Haynes, Anne‐Maree ; Ting, Francis ; Barentsz, Jelle ; Roobol, Monique ; Vass, Justin ; Rasiah, Krishan ; Delprado, Warick ; Stricker, Phillip D.</creatorcontrib><description>Objective
To develop and externally validate a predictive model for detection of significant prostate cancer.
Patients and Methods
Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate‐specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling.
Results
In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer.
Conclusions
Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over‐detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.</description><identifier>ISSN: 1464-4096</identifier><identifier>EISSN: 1464-410X</identifier><identifier>DOI: 10.1111/bju.13814</identifier><identifier>PMID: 28207981</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Aged ; Biopsy ; Biopsy - statistics & numerical data ; Data processing ; early detection ; Early Detection of Cancer - statistics & numerical data ; Humans ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; mpMRI ; NMR ; nomogram ; Nomograms ; Nuclear magnetic resonance ; PCSM ; Prospective Studies ; Prostate - diagnostic imaging ; Prostate - pathology ; Prostate cancer ; Prostate-specific antigen ; ProstateCancer ; Prostatic Neoplasms - diagnostic imaging ; Prostatic Neoplasms - epidemiology ; Prostatic Neoplasms - pathology ; Rectum ; Risk Assessment ; screening ; Statistical analysis</subject><ispartof>BJU international, 2017-12, Vol.120 (6), p.774-781</ispartof><rights>2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd</rights><rights>2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.</rights><rights>BJUI © 2017 BJU International</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3884-d12a061affe9695a9eb05761727be7de2f323855ecf21b62d94d419fcd0d261a3</citedby><cites>FETCH-LOGICAL-c3884-d12a061affe9695a9eb05761727be7de2f323855ecf21b62d94d419fcd0d261a3</cites><orcidid>0000-0002-5531-6104 ; 0000-0002-5805-9860</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fbju.13814$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fbju.13814$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28207981$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leeuwen, Pim J.</creatorcontrib><creatorcontrib>Hayen, Andrew</creatorcontrib><creatorcontrib>Thompson, James E.</creatorcontrib><creatorcontrib>Moses, Daniel</creatorcontrib><creatorcontrib>Shnier, Ron</creatorcontrib><creatorcontrib>Böhm, Maret</creatorcontrib><creatorcontrib>Abuodha, Magdaline</creatorcontrib><creatorcontrib>Haynes, Anne‐Maree</creatorcontrib><creatorcontrib>Ting, Francis</creatorcontrib><creatorcontrib>Barentsz, Jelle</creatorcontrib><creatorcontrib>Roobol, Monique</creatorcontrib><creatorcontrib>Vass, Justin</creatorcontrib><creatorcontrib>Rasiah, Krishan</creatorcontrib><creatorcontrib>Delprado, Warick</creatorcontrib><creatorcontrib>Stricker, Phillip D.</creatorcontrib><title>A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy</title><title>BJU international</title><addtitle>BJU Int</addtitle><description>Objective
To develop and externally validate a predictive model for detection of significant prostate cancer.
Patients and Methods
Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate‐specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling.
Results
In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer.
Conclusions
Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over‐detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.</description><subject>Aged</subject><subject>Biopsy</subject><subject>Biopsy - statistics & numerical data</subject><subject>Data processing</subject><subject>early detection</subject><subject>Early Detection of Cancer - statistics & numerical data</subject><subject>Humans</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>mpMRI</subject><subject>NMR</subject><subject>nomogram</subject><subject>Nomograms</subject><subject>Nuclear magnetic resonance</subject><subject>PCSM</subject><subject>Prospective Studies</subject><subject>Prostate - diagnostic imaging</subject><subject>Prostate - pathology</subject><subject>Prostate cancer</subject><subject>Prostate-specific antigen</subject><subject>ProstateCancer</subject><subject>Prostatic Neoplasms - diagnostic imaging</subject><subject>Prostatic Neoplasms - epidemiology</subject><subject>Prostatic Neoplasms - pathology</subject><subject>Rectum</subject><subject>Risk Assessment</subject><subject>screening</subject><subject>Statistical analysis</subject><issn>1464-4096</issn><issn>1464-410X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kbFuFDEQhi1EREKg4AWQJRooLrG9u167TCICiSLREInO8trjw8eufdheoeso8gA8I0-Cj00oIjGNxzPf_JrRj9ArSk5ojdNhM5_QRtD2CTqiLW9XLSVfnj7kRPJD9DznDSG1wLtn6JAJRnop6BG6O8PTPBa_1UlPUJI3eNLrAKUmCXIMOhjAvtZ8WP_--WvQGSxOPn_DU7Qw4hKxhQJp8gFw-QpLLzqc_Tp4540OBW9TzEUXwGYvl-rfx7QfHXzc5t0LdOD0mOHl_XuMbi_ff774uLr59OHq4uxmZRoh2pWlTBNOtXMguey0hIF0Pac96wfoLTDXsEZ0HRjH6MCZla1tqXTGEsvqXHOM3i66dZ_vM-SiJp8NjKMOEOesqOBSctF0XUXfPEI3cU6hbqeo5D0TLWNNpd4tlKkH5gRO1csmnXaKErW3RlVr1F9rKvv6XnEeJrD_yAcvKnC6AD_8CLv_K6nz69tF8g_cu5sN</recordid><startdate>201712</startdate><enddate>201712</enddate><creator>Leeuwen, Pim J.</creator><creator>Hayen, Andrew</creator><creator>Thompson, James E.</creator><creator>Moses, Daniel</creator><creator>Shnier, Ron</creator><creator>Böhm, Maret</creator><creator>Abuodha, Magdaline</creator><creator>Haynes, Anne‐Maree</creator><creator>Ting, Francis</creator><creator>Barentsz, Jelle</creator><creator>Roobol, Monique</creator><creator>Vass, Justin</creator><creator>Rasiah, Krishan</creator><creator>Delprado, Warick</creator><creator>Stricker, Phillip D.</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5531-6104</orcidid><orcidid>https://orcid.org/0000-0002-5805-9860</orcidid></search><sort><creationdate>201712</creationdate><title>A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy</title><author>Leeuwen, Pim J. ; Hayen, Andrew ; Thompson, James E. ; Moses, Daniel ; Shnier, Ron ; Böhm, Maret ; Abuodha, Magdaline ; Haynes, Anne‐Maree ; Ting, Francis ; Barentsz, Jelle ; Roobol, Monique ; Vass, Justin ; Rasiah, Krishan ; Delprado, Warick ; Stricker, Phillip D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3884-d12a061affe9695a9eb05761727be7de2f323855ecf21b62d94d419fcd0d261a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aged</topic><topic>Biopsy</topic><topic>Biopsy - statistics & numerical data</topic><topic>Data processing</topic><topic>early detection</topic><topic>Early Detection of Cancer - statistics & numerical data</topic><topic>Humans</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>mpMRI</topic><topic>NMR</topic><topic>nomogram</topic><topic>Nomograms</topic><topic>Nuclear magnetic resonance</topic><topic>PCSM</topic><topic>Prospective Studies</topic><topic>Prostate - diagnostic imaging</topic><topic>Prostate - pathology</topic><topic>Prostate cancer</topic><topic>Prostate-specific antigen</topic><topic>ProstateCancer</topic><topic>Prostatic Neoplasms - diagnostic imaging</topic><topic>Prostatic Neoplasms - epidemiology</topic><topic>Prostatic Neoplasms - pathology</topic><topic>Rectum</topic><topic>Risk Assessment</topic><topic>screening</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leeuwen, Pim J.</creatorcontrib><creatorcontrib>Hayen, Andrew</creatorcontrib><creatorcontrib>Thompson, James E.</creatorcontrib><creatorcontrib>Moses, Daniel</creatorcontrib><creatorcontrib>Shnier, Ron</creatorcontrib><creatorcontrib>Böhm, Maret</creatorcontrib><creatorcontrib>Abuodha, Magdaline</creatorcontrib><creatorcontrib>Haynes, Anne‐Maree</creatorcontrib><creatorcontrib>Ting, Francis</creatorcontrib><creatorcontrib>Barentsz, Jelle</creatorcontrib><creatorcontrib>Roobol, Monique</creatorcontrib><creatorcontrib>Vass, Justin</creatorcontrib><creatorcontrib>Rasiah, Krishan</creatorcontrib><creatorcontrib>Delprado, Warick</creatorcontrib><creatorcontrib>Stricker, Phillip D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>BJU international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leeuwen, Pim J.</au><au>Hayen, Andrew</au><au>Thompson, James E.</au><au>Moses, Daniel</au><au>Shnier, Ron</au><au>Böhm, Maret</au><au>Abuodha, Magdaline</au><au>Haynes, Anne‐Maree</au><au>Ting, Francis</au><au>Barentsz, Jelle</au><au>Roobol, Monique</au><au>Vass, Justin</au><au>Rasiah, Krishan</au><au>Delprado, Warick</au><au>Stricker, Phillip D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy</atitle><jtitle>BJU international</jtitle><addtitle>BJU Int</addtitle><date>2017-12</date><risdate>2017</risdate><volume>120</volume><issue>6</issue><spage>774</spage><epage>781</epage><pages>774-781</pages><issn>1464-4096</issn><eissn>1464-410X</eissn><abstract>Objective
To develop and externally validate a predictive model for detection of significant prostate cancer.
Patients and Methods
Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate‐specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling.
Results
In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer.
Conclusions
Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over‐detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>28207981</pmid><doi>10.1111/bju.13814</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-5531-6104</orcidid><orcidid>https://orcid.org/0000-0002-5805-9860</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1464-4096 |
| ispartof | BJU international, 2017-12, Vol.120 (6), p.774-781 |
| issn | 1464-4096 1464-410X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1869968355 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Aged Biopsy Biopsy - statistics & numerical data Data processing early detection Early Detection of Cancer - statistics & numerical data Humans Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Middle Aged mpMRI NMR nomogram Nomograms Nuclear magnetic resonance PCSM Prospective Studies Prostate - diagnostic imaging Prostate - pathology Prostate cancer Prostate-specific antigen ProstateCancer Prostatic Neoplasms - diagnostic imaging Prostatic Neoplasms - epidemiology Prostatic Neoplasms - pathology Rectum Risk Assessment screening Statistical analysis |
| title | A multiparametric magnetic resonance imaging‐based risk model to determine the risk of significant prostate cancer prior to biopsy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T19%3A54%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20multiparametric%20magnetic%20resonance%20imaging%E2%80%90based%20risk%20model%20to%20determine%20the%20risk%20of%20significant%20prostate%20cancer%20prior%20to%20biopsy&rft.jtitle=BJU%20international&rft.au=Leeuwen,%20Pim%20J.&rft.date=2017-12&rft.volume=120&rft.issue=6&rft.spage=774&rft.epage=781&rft.pages=774-781&rft.issn=1464-4096&rft.eissn=1464-410X&rft_id=info:doi/10.1111/bju.13814&rft_dat=%3Cproquest_cross%3E1967284223%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1967284223&rft_id=info:pmid/28207981&rfr_iscdi=true |