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Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study
Background The high level of expertise required for accurate interpretation of prostate MRI. Purpose To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. Study Type Retrospective. Subjects One thousand two hundred thirty...
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| Published in: | Journal of magnetic resonance imaging 2023-05, Vol.57 (5), p.1352-1364 |
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| container_title | Journal of magnetic resonance imaging |
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| creator | Jiang, Ke‐Wen Song, Yang Hou, Ying Zhi, Rui Zhang, Jing Bao, Mei‐Ling Li, Hai Yan, Xu Xi, Wei Zhang, Cheng‐Xiu Yao, Ye‐Feng Yang, Guang Zhang, Yu‐Dong |
| description | Background
The high level of expertise required for accurate interpretation of prostate MRI.
Purpose
To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.
Study Type
Retrospective.
Subjects
One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U‐Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).
Field Strength/Sequence
3.0T/scanners, T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and apparent diffusion coefficient map.
Assessment
Close‐loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.
Statistical Tests
Area under the receiver operating characteristic curve (AUC‐ROC); Delong test; Meta‐regression I2 analysis.
Results
In average, for internal test, AI had lower AUC‐ROC than subspecialists (0.85 vs. 0.92, P |
| doi_str_mv | 10.1002/jmri.28427 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2724240042</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2724240042</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3577-520d31b2123637400a155ddaa6ce5d1fe4c1a137437a910b9f784348ae2c6d9c3</originalsourceid><addsrcrecordid>eNp9kcFu1DAQhiMEoqVw4QGQJS4IKcUe23HCbRVo2aqIioVz5LWdxavEXmxHVW48Auoj9knwsm0PHDh5pPnmk2f-onhJ8CnBGN5tx2BPoWYgHhXHhAOUwOvqca4xpyWpsTgqnsW4xRg3DeNPiyNaAQAFdlzcXJnQ-zBKpwzyPVqEZHurrBzQ0iUzDHZjcuv21--F1UajD1ZunI82otUckxlRHkbtYJ1VchhmtLIbtxdIl9BV8DHJZFC7twd0bdMP9Pnr8j1a3HuSVaj1404GG71DqzTp-XnxpJdDNC_u3pPi-9nHb-2n8vLL-bJdXJaKciFKDlhTsgYCtKKCYSwJ51pLWSnDNekNU0SS3KFCNgSvm17UjLJaGlCVbhQ9Kd4cvLvgf04mpm60UeWVpTN-ih0IYJC9DDL6-h9066fg8u8y1fCmaggTmXp7oFRePAbTd7tgRxnmjuBun1S3T6r7m1SGX90pp_Vo9AN6H00GyAG4toOZ_6PqLvJND9I_z0-fvA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2795969147</pqid></control><display><type>article</type><title>Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study</title><source>Wiley-Blackwell Read & Publish Collection</source><creator>Jiang, Ke‐Wen ; Song, Yang ; Hou, Ying ; Zhi, Rui ; Zhang, Jing ; Bao, Mei‐Ling ; Li, Hai ; Yan, Xu ; Xi, Wei ; Zhang, Cheng‐Xiu ; Yao, Ye‐Feng ; Yang, Guang ; Zhang, Yu‐Dong</creator><creatorcontrib>Jiang, Ke‐Wen ; Song, Yang ; Hou, Ying ; Zhi, Rui ; Zhang, Jing ; Bao, Mei‐Ling ; Li, Hai ; Yan, Xu ; Xi, Wei ; Zhang, Cheng‐Xiu ; Yao, Ye‐Feng ; Yang, Guang ; Zhang, Yu‐Dong</creatorcontrib><description>Background
The high level of expertise required for accurate interpretation of prostate MRI.
Purpose
To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.
Study Type
Retrospective.
Subjects
One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U‐Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).
Field Strength/Sequence
3.0T/scanners, T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and apparent diffusion coefficient map.
Assessment
Close‐loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.
Statistical Tests
Area under the receiver operating characteristic curve (AUC‐ROC); Delong test; Meta‐regression I2 analysis.
Results
In average, for internal test, AI had lower AUC‐ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel–Haenszel I2 = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI‐RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05).
Data Conclusion
Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI.
Evidence Level
3
Technical Efficacy
Stage 2</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.28427</identifier><identifier>PMID: 36222324</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Artificial Intelligence ; biparametric MRI ; Clinical significance ; clinically significant prostate cancer ; deep learning ; Diagnosis ; Diagnostic systems ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; Field strength ; Heterogeneity ; Humans ; Image segmentation ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Prostate cancer ; Prostatic Neoplasms - pathology ; Retrospective Studies ; Statistical analysis ; Statistical tests ; the Prostate Imaging Reporting and Data System ; Training</subject><ispartof>Journal of magnetic resonance imaging, 2023-05, Vol.57 (5), p.1352-1364</ispartof><rights>2022 International Society for Magnetic Resonance in Medicine.</rights><rights>2023 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3577-520d31b2123637400a155ddaa6ce5d1fe4c1a137437a910b9f784348ae2c6d9c3</citedby><cites>FETCH-LOGICAL-c3577-520d31b2123637400a155ddaa6ce5d1fe4c1a137437a910b9f784348ae2c6d9c3</cites><orcidid>0000-0002-2811-7513 ; 0000-0001-8356-567X ; 0000-0001-8942-427X ; 0000-0001-7426-4496</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36222324$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jiang, Ke‐Wen</creatorcontrib><creatorcontrib>Song, Yang</creatorcontrib><creatorcontrib>Hou, Ying</creatorcontrib><creatorcontrib>Zhi, Rui</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><creatorcontrib>Bao, Mei‐Ling</creatorcontrib><creatorcontrib>Li, Hai</creatorcontrib><creatorcontrib>Yan, Xu</creatorcontrib><creatorcontrib>Xi, Wei</creatorcontrib><creatorcontrib>Zhang, Cheng‐Xiu</creatorcontrib><creatorcontrib>Yao, Ye‐Feng</creatorcontrib><creatorcontrib>Yang, Guang</creatorcontrib><creatorcontrib>Zhang, Yu‐Dong</creatorcontrib><title>Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
The high level of expertise required for accurate interpretation of prostate MRI.
Purpose
To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.
Study Type
Retrospective.
Subjects
One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U‐Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).
Field Strength/Sequence
3.0T/scanners, T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and apparent diffusion coefficient map.
Assessment
Close‐loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.
Statistical Tests
Area under the receiver operating characteristic curve (AUC‐ROC); Delong test; Meta‐regression I2 analysis.
Results
In average, for internal test, AI had lower AUC‐ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel–Haenszel I2 = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI‐RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05).
Data Conclusion
Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI.
Evidence Level
3
Technical Efficacy
Stage 2</description><subject>Artificial Intelligence</subject><subject>biparametric MRI</subject><subject>Clinical significance</subject><subject>clinically significant prostate cancer</subject><subject>deep learning</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Field strength</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Image segmentation</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - pathology</subject><subject>Retrospective Studies</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>the Prostate Imaging Reporting and Data System</subject><subject>Training</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kcFu1DAQhiMEoqVw4QGQJS4IKcUe23HCbRVo2aqIioVz5LWdxavEXmxHVW48Auoj9knwsm0PHDh5pPnmk2f-onhJ8CnBGN5tx2BPoWYgHhXHhAOUwOvqca4xpyWpsTgqnsW4xRg3DeNPiyNaAQAFdlzcXJnQ-zBKpwzyPVqEZHurrBzQ0iUzDHZjcuv21--F1UajD1ZunI82otUckxlRHkbtYJ1VchhmtLIbtxdIl9BV8DHJZFC7twd0bdMP9Pnr8j1a3HuSVaj1404GG71DqzTp-XnxpJdDNC_u3pPi-9nHb-2n8vLL-bJdXJaKciFKDlhTsgYCtKKCYSwJ51pLWSnDNekNU0SS3KFCNgSvm17UjLJaGlCVbhQ9Kd4cvLvgf04mpm60UeWVpTN-ih0IYJC9DDL6-h9066fg8u8y1fCmaggTmXp7oFRePAbTd7tgRxnmjuBun1S3T6r7m1SGX90pp_Vo9AN6H00GyAG4toOZ_6PqLvJND9I_z0-fvA</recordid><startdate>202305</startdate><enddate>202305</enddate><creator>Jiang, Ke‐Wen</creator><creator>Song, Yang</creator><creator>Hou, Ying</creator><creator>Zhi, Rui</creator><creator>Zhang, Jing</creator><creator>Bao, Mei‐Ling</creator><creator>Li, Hai</creator><creator>Yan, Xu</creator><creator>Xi, Wei</creator><creator>Zhang, Cheng‐Xiu</creator><creator>Yao, Ye‐Feng</creator><creator>Yang, Guang</creator><creator>Zhang, Yu‐Dong</creator><general>John Wiley & Sons, Inc</general><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>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2811-7513</orcidid><orcidid>https://orcid.org/0000-0001-8356-567X</orcidid><orcidid>https://orcid.org/0000-0001-8942-427X</orcidid><orcidid>https://orcid.org/0000-0001-7426-4496</orcidid></search><sort><creationdate>202305</creationdate><title>Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study</title><author>Jiang, Ke‐Wen ; Song, Yang ; Hou, Ying ; Zhi, Rui ; Zhang, Jing ; Bao, Mei‐Ling ; Li, Hai ; Yan, Xu ; Xi, Wei ; Zhang, Cheng‐Xiu ; Yao, Ye‐Feng ; Yang, Guang ; Zhang, Yu‐Dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3577-520d31b2123637400a155ddaa6ce5d1fe4c1a137437a910b9f784348ae2c6d9c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>biparametric MRI</topic><topic>Clinical significance</topic><topic>clinically significant prostate cancer</topic><topic>deep learning</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Field strength</topic><topic>Heterogeneity</topic><topic>Humans</topic><topic>Image segmentation</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - pathology</topic><topic>Retrospective Studies</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>the Prostate Imaging Reporting and Data System</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Ke‐Wen</creatorcontrib><creatorcontrib>Song, Yang</creatorcontrib><creatorcontrib>Hou, Ying</creatorcontrib><creatorcontrib>Zhi, Rui</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><creatorcontrib>Bao, Mei‐Ling</creatorcontrib><creatorcontrib>Li, Hai</creatorcontrib><creatorcontrib>Yan, Xu</creatorcontrib><creatorcontrib>Xi, Wei</creatorcontrib><creatorcontrib>Zhang, Cheng‐Xiu</creatorcontrib><creatorcontrib>Yao, Ye‐Feng</creatorcontrib><creatorcontrib>Yang, Guang</creatorcontrib><creatorcontrib>Zhang, Yu‐Dong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Ke‐Wen</au><au>Song, Yang</au><au>Hou, Ying</au><au>Zhi, Rui</au><au>Zhang, Jing</au><au>Bao, Mei‐Ling</au><au>Li, Hai</au><au>Yan, Xu</au><au>Xi, Wei</au><au>Zhang, Cheng‐Xiu</au><au>Yao, Ye‐Feng</au><au>Yang, Guang</au><au>Zhang, Yu‐Dong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2023-05</date><risdate>2023</risdate><volume>57</volume><issue>5</issue><spage>1352</spage><epage>1364</epage><pages>1352-1364</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
The high level of expertise required for accurate interpretation of prostate MRI.
Purpose
To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.
Study Type
Retrospective.
Subjects
One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U‐Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).
Field Strength/Sequence
3.0T/scanners, T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and apparent diffusion coefficient map.
Assessment
Close‐loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.
Statistical Tests
Area under the receiver operating characteristic curve (AUC‐ROC); Delong test; Meta‐regression I2 analysis.
Results
In average, for internal test, AI had lower AUC‐ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel–Haenszel I2 = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI‐RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05).
Data Conclusion
Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI.
Evidence Level
3
Technical Efficacy
Stage 2</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36222324</pmid><doi>10.1002/jmri.28427</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2811-7513</orcidid><orcidid>https://orcid.org/0000-0001-8356-567X</orcidid><orcidid>https://orcid.org/0000-0001-8942-427X</orcidid><orcidid>https://orcid.org/0000-0001-7426-4496</orcidid></addata></record> |
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| identifier | ISSN: 1053-1807 |
| ispartof | Journal of magnetic resonance imaging, 2023-05, Vol.57 (5), p.1352-1364 |
| issn | 1053-1807 1522-2586 |
| language | eng |
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| source | Wiley-Blackwell Read & Publish Collection |
| subjects | Artificial Intelligence biparametric MRI Clinical significance clinically significant prostate cancer deep learning Diagnosis Diagnostic systems Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods Field strength Heterogeneity Humans Image segmentation Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Prostate cancer Prostatic Neoplasms - pathology Retrospective Studies Statistical analysis Statistical tests the Prostate Imaging Reporting and Data System Training |
| title | Performance of Artificial Intelligence‐Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study |
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