Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness
Background Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. Purpose To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2022-06, Vol.55 (6), p.1745-1758 |
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| creator | McGarry, Sean D. Brehler, Michael Bukowy, John D. Lowman, Allison K. Bobholz, Samuel A. Duenweg, Savannah R. Banerjee, Anjishnu Hurrell, Sarah L. Malyarenko, Dariya Chenevert, Thomas L. Cao, Yue Li, Yuan You, Daekeun Fedorov, Andrey Bell, Laura C. Quarles, C. Chad Prah, Melissa A. Schmainda, Kathleen M. Taouli, Bachir LoCastro, Eve Mazaheri, Yousef Shukla‐Dave, Amita Yankeelov, Thomas E. Hormuth, David A. Madhuranthakam, Ananth J. Hulsey, Keith Li, Kurt Huang, Wei Huang, Wei Muzi, Mark Jacobs, Michael A. Solaiyappan, Meiyappan Hectors, Stefanie Antic, Tatjana Paner, Gladell P. Palangmonthip, Watchareepohn Jacobsohn, Kenneth Hohenwalter, Mark Duvnjak, Petar Griffin, Michael See, William Nevalainen, Marja T. Iczkowski, Kenneth A. LaViolette, Peter S. |
| description | Background
Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.
Purpose
To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.
Study Type
Prospective.
Population
Thirty‐three patients prospectively imaged prior to prostatectomy.
Field Strength/Sequence
3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.
Assessment
Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).
Statistical Test
Levene's test, P |
| doi_str_mv | 10.1002/jmri.27983 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2597491136</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2597491136</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3933-dce551800c61f386c2a01043e9e3ec74aff00fdd5c3cc5909cf896c00a7954483</originalsourceid><addsrcrecordid>eNp9kctu1TAURa0K1Bed8AEoEpOqUsrxM_awuuVxUSterTq0jGMHXyVxayegzvgEvpEvweEWBgwYHUt7aen4bISeYjjFAOTFZkjhlDRK0h20jzkhNeFSPCpv4LTGEpo9dJDzBgCUYnwX7VHWiEZIso-my7mfws_vPz6FyVWrONqYWjNaV0VfnQfv5xziWPIbF7ovk2ur9WC6MHbVh9mMU_DBmqkQlY-pOsvZ5byE71PMk1mMi6skXZeW6Ksby3iCHnvTZ3f0MA_R9auXV6s39cW71-vV2UVtqaK0bq3jvKwPVmBPpbDEAAZGnXLU2YYZ7wF823JLreUKlPVSCQtgGsUZk_QQHW-9tynezS5PegjZur43o4tz1oSrhimMqSjo83_QTZzTWLbTRAiCqWSSFepkS9nyv5yc17cpDCbdawx66UIvXejfXRT42YNy_jy49i_65_gFwFvgW-jd_X9U-u3lx_VW-gte1JdR</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2662138484</pqid></control><display><type>article</type><title>Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>McGarry, Sean D. ; Brehler, Michael ; Bukowy, John D. ; Lowman, Allison K. ; Bobholz, Samuel A. ; Duenweg, Savannah R. ; Banerjee, Anjishnu ; Hurrell, Sarah L. ; Malyarenko, Dariya ; Chenevert, Thomas L. ; Cao, Yue ; Li, Yuan ; You, Daekeun ; Fedorov, Andrey ; Bell, Laura C. ; Quarles, C. Chad ; Prah, Melissa A. ; Schmainda, Kathleen M. ; Taouli, Bachir ; LoCastro, Eve ; Mazaheri, Yousef ; Shukla‐Dave, Amita ; Yankeelov, Thomas E. ; Hormuth, David A. ; Madhuranthakam, Ananth J. ; Hulsey, Keith ; Li, Kurt ; Huang, Wei ; Huang, Wei ; Muzi, Mark ; Jacobs, Michael A. ; Solaiyappan, Meiyappan ; Hectors, Stefanie ; Antic, Tatjana ; Paner, Gladell P. ; Palangmonthip, Watchareepohn ; Jacobsohn, Kenneth ; Hohenwalter, Mark ; Duvnjak, Petar ; Griffin, Michael ; See, William ; Nevalainen, Marja T. ; Iczkowski, Kenneth A. ; LaViolette, Peter S.</creator><creatorcontrib>McGarry, Sean D. ; Brehler, Michael ; Bukowy, John D. ; Lowman, Allison K. ; Bobholz, Samuel A. ; Duenweg, Savannah R. ; Banerjee, Anjishnu ; Hurrell, Sarah L. ; Malyarenko, Dariya ; Chenevert, Thomas L. ; Cao, Yue ; Li, Yuan ; You, Daekeun ; Fedorov, Andrey ; Bell, Laura C. ; Quarles, C. Chad ; Prah, Melissa A. ; Schmainda, Kathleen M. ; Taouli, Bachir ; LoCastro, Eve ; Mazaheri, Yousef ; Shukla‐Dave, Amita ; Yankeelov, Thomas E. ; Hormuth, David A. ; Madhuranthakam, Ananth J. ; Hulsey, Keith ; Li, Kurt ; Huang, Wei ; Huang, Wei ; Muzi, Mark ; Jacobs, Michael A. ; Solaiyappan, Meiyappan ; Hectors, Stefanie ; Antic, Tatjana ; Paner, Gladell P. ; Palangmonthip, Watchareepohn ; Jacobsohn, Kenneth ; Hohenwalter, Mark ; Duvnjak, Petar ; Griffin, Michael ; See, William ; Nevalainen, Marja T. ; Iczkowski, Kenneth A. ; LaViolette, Peter S.</creatorcontrib><description>Background
Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.
Purpose
To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.
Study Type
Prospective.
Population
Thirty‐three patients prospectively imaged prior to prostatectomy.
Field Strength/Sequence
3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.
Assessment
Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).
Statistical Test
Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.
Results
The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.
Data Conclusion
We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer.
Level of Evidence
1
Technical Efficacy
Stage 3</description><identifier>ISSN: 1053-1807</identifier><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.27983</identifier><identifier>PMID: 34767682</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; cancer ; Cancer surgery ; Datasets ; Differentiation ; Diffusion ; Diffusion coefficient ; Diffusion Magnetic Resonance Imaging - methods ; Discordance ; Field strength ; Humans ; Kurtosis ; Magnetic resonance imaging ; Male ; Medical imaging ; MRI ; multisite |modelling ; Perfusion ; Population studies ; Process parameters ; Prospective Studies ; prostate ; Prostate cancer ; Prostatectomy ; Prostatic Neoplasms - diagnostic imaging ; Retrospective Studies ; ROC Curve ; Sensitivity and Specificity ; Statistical analysis ; Statistical tests ; Urological surgery</subject><ispartof>Journal of magnetic resonance imaging, 2022-06, Vol.55 (6), p.1745-1758</ispartof><rights>2021 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3933-dce551800c61f386c2a01043e9e3ec74aff00fdd5c3cc5909cf896c00a7954483</citedby><cites>FETCH-LOGICAL-c3933-dce551800c61f386c2a01043e9e3ec74aff00fdd5c3cc5909cf896c00a7954483</cites><orcidid>0000-0003-3975-8153 ; 0000-0002-9602-6891 ; 0000-0001-7456-3197 ; 0000-0001-6409-1333</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.27983$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.27983$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,1412,27905,27906,45555,45556</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34767682$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McGarry, Sean D.</creatorcontrib><creatorcontrib>Brehler, Michael</creatorcontrib><creatorcontrib>Bukowy, John D.</creatorcontrib><creatorcontrib>Lowman, Allison K.</creatorcontrib><creatorcontrib>Bobholz, Samuel A.</creatorcontrib><creatorcontrib>Duenweg, Savannah R.</creatorcontrib><creatorcontrib>Banerjee, Anjishnu</creatorcontrib><creatorcontrib>Hurrell, Sarah L.</creatorcontrib><creatorcontrib>Malyarenko, Dariya</creatorcontrib><creatorcontrib>Chenevert, Thomas L.</creatorcontrib><creatorcontrib>Cao, Yue</creatorcontrib><creatorcontrib>Li, Yuan</creatorcontrib><creatorcontrib>You, Daekeun</creatorcontrib><creatorcontrib>Fedorov, Andrey</creatorcontrib><creatorcontrib>Bell, Laura C.</creatorcontrib><creatorcontrib>Quarles, C. Chad</creatorcontrib><creatorcontrib>Prah, Melissa A.</creatorcontrib><creatorcontrib>Schmainda, Kathleen M.</creatorcontrib><creatorcontrib>Taouli, Bachir</creatorcontrib><creatorcontrib>LoCastro, Eve</creatorcontrib><creatorcontrib>Mazaheri, Yousef</creatorcontrib><creatorcontrib>Shukla‐Dave, Amita</creatorcontrib><creatorcontrib>Yankeelov, Thomas E.</creatorcontrib><creatorcontrib>Hormuth, David A.</creatorcontrib><creatorcontrib>Madhuranthakam, Ananth J.</creatorcontrib><creatorcontrib>Hulsey, Keith</creatorcontrib><creatorcontrib>Li, Kurt</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Muzi, Mark</creatorcontrib><creatorcontrib>Jacobs, Michael A.</creatorcontrib><creatorcontrib>Solaiyappan, Meiyappan</creatorcontrib><creatorcontrib>Hectors, Stefanie</creatorcontrib><creatorcontrib>Antic, Tatjana</creatorcontrib><creatorcontrib>Paner, Gladell P.</creatorcontrib><creatorcontrib>Palangmonthip, Watchareepohn</creatorcontrib><creatorcontrib>Jacobsohn, Kenneth</creatorcontrib><creatorcontrib>Hohenwalter, Mark</creatorcontrib><creatorcontrib>Duvnjak, Petar</creatorcontrib><creatorcontrib>Griffin, Michael</creatorcontrib><creatorcontrib>See, William</creatorcontrib><creatorcontrib>Nevalainen, Marja T.</creatorcontrib><creatorcontrib>Iczkowski, Kenneth A.</creatorcontrib><creatorcontrib>LaViolette, Peter S.</creatorcontrib><title>Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.
Purpose
To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.
Study Type
Prospective.
Population
Thirty‐three patients prospectively imaged prior to prostatectomy.
Field Strength/Sequence
3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.
Assessment
Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).
Statistical Test
Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.
Results
The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.
Data Conclusion
We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer.
Level of Evidence
1
Technical Efficacy
Stage 3</description><subject>Algorithms</subject><subject>cancer</subject><subject>Cancer surgery</subject><subject>Datasets</subject><subject>Differentiation</subject><subject>Diffusion</subject><subject>Diffusion coefficient</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Discordance</subject><subject>Field strength</subject><subject>Humans</subject><subject>Kurtosis</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Medical imaging</subject><subject>MRI</subject><subject>multisite |modelling</subject><subject>Perfusion</subject><subject>Population studies</subject><subject>Process parameters</subject><subject>Prospective Studies</subject><subject>prostate</subject><subject>Prostate cancer</subject><subject>Prostatectomy</subject><subject>Prostatic Neoplasms - diagnostic imaging</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Sensitivity and Specificity</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Urological surgery</subject><issn>1053-1807</issn><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp9kctu1TAURa0K1Bed8AEoEpOqUsrxM_awuuVxUSterTq0jGMHXyVxayegzvgEvpEvweEWBgwYHUt7aen4bISeYjjFAOTFZkjhlDRK0h20jzkhNeFSPCpv4LTGEpo9dJDzBgCUYnwX7VHWiEZIso-my7mfws_vPz6FyVWrONqYWjNaV0VfnQfv5xziWPIbF7ovk2ur9WC6MHbVh9mMU_DBmqkQlY-pOsvZ5byE71PMk1mMi6skXZeW6Ksby3iCHnvTZ3f0MA_R9auXV6s39cW71-vV2UVtqaK0bq3jvKwPVmBPpbDEAAZGnXLU2YYZ7wF823JLreUKlPVSCQtgGsUZk_QQHW-9tynezS5PegjZur43o4tz1oSrhimMqSjo83_QTZzTWLbTRAiCqWSSFepkS9nyv5yc17cpDCbdawx66UIvXejfXRT42YNy_jy49i_65_gFwFvgW-jd_X9U-u3lx_VW-gte1JdR</recordid><startdate>202206</startdate><enddate>202206</enddate><creator>McGarry, Sean D.</creator><creator>Brehler, Michael</creator><creator>Bukowy, John D.</creator><creator>Lowman, Allison K.</creator><creator>Bobholz, Samuel A.</creator><creator>Duenweg, Savannah R.</creator><creator>Banerjee, Anjishnu</creator><creator>Hurrell, Sarah L.</creator><creator>Malyarenko, Dariya</creator><creator>Chenevert, Thomas L.</creator><creator>Cao, Yue</creator><creator>Li, Yuan</creator><creator>You, Daekeun</creator><creator>Fedorov, Andrey</creator><creator>Bell, Laura C.</creator><creator>Quarles, C. Chad</creator><creator>Prah, Melissa A.</creator><creator>Schmainda, Kathleen M.</creator><creator>Taouli, Bachir</creator><creator>LoCastro, Eve</creator><creator>Mazaheri, Yousef</creator><creator>Shukla‐Dave, Amita</creator><creator>Yankeelov, Thomas E.</creator><creator>Hormuth, David A.</creator><creator>Madhuranthakam, Ananth J.</creator><creator>Hulsey, Keith</creator><creator>Li, Kurt</creator><creator>Huang, Wei</creator><creator>Huang, Wei</creator><creator>Muzi, Mark</creator><creator>Jacobs, Michael A.</creator><creator>Solaiyappan, Meiyappan</creator><creator>Hectors, Stefanie</creator><creator>Antic, Tatjana</creator><creator>Paner, Gladell P.</creator><creator>Palangmonthip, Watchareepohn</creator><creator>Jacobsohn, Kenneth</creator><creator>Hohenwalter, Mark</creator><creator>Duvnjak, Petar</creator><creator>Griffin, Michael</creator><creator>See, William</creator><creator>Nevalainen, Marja T.</creator><creator>Iczkowski, Kenneth A.</creator><creator>LaViolette, Peter S.</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><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/0000-0003-3975-8153</orcidid><orcidid>https://orcid.org/0000-0002-9602-6891</orcidid><orcidid>https://orcid.org/0000-0001-7456-3197</orcidid><orcidid>https://orcid.org/0000-0001-6409-1333</orcidid></search><sort><creationdate>202206</creationdate><title>Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness</title><author>McGarry, Sean D. ; Brehler, Michael ; Bukowy, John D. ; Lowman, Allison K. ; Bobholz, Samuel A. ; Duenweg, Savannah R. ; Banerjee, Anjishnu ; Hurrell, Sarah L. ; Malyarenko, Dariya ; Chenevert, Thomas L. ; Cao, Yue ; Li, Yuan ; You, Daekeun ; Fedorov, Andrey ; Bell, Laura C. ; Quarles, C. Chad ; Prah, Melissa A. ; Schmainda, Kathleen M. ; Taouli, Bachir ; LoCastro, Eve ; Mazaheri, Yousef ; Shukla‐Dave, Amita ; Yankeelov, Thomas E. ; Hormuth, David A. ; Madhuranthakam, Ananth J. ; Hulsey, Keith ; Li, Kurt ; Huang, Wei ; Huang, Wei ; Muzi, Mark ; Jacobs, Michael A. ; Solaiyappan, Meiyappan ; Hectors, Stefanie ; Antic, Tatjana ; Paner, Gladell P. ; Palangmonthip, Watchareepohn ; Jacobsohn, Kenneth ; Hohenwalter, Mark ; Duvnjak, Petar ; Griffin, Michael ; See, William ; Nevalainen, Marja T. ; Iczkowski, Kenneth A. ; LaViolette, Peter S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3933-dce551800c61f386c2a01043e9e3ec74aff00fdd5c3cc5909cf896c00a7954483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>cancer</topic><topic>Cancer surgery</topic><topic>Datasets</topic><topic>Differentiation</topic><topic>Diffusion</topic><topic>Diffusion coefficient</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Discordance</topic><topic>Field strength</topic><topic>Humans</topic><topic>Kurtosis</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Medical imaging</topic><topic>MRI</topic><topic>multisite |modelling</topic><topic>Perfusion</topic><topic>Population studies</topic><topic>Process parameters</topic><topic>Prospective Studies</topic><topic>prostate</topic><topic>Prostate cancer</topic><topic>Prostatectomy</topic><topic>Prostatic Neoplasms - diagnostic imaging</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Sensitivity and Specificity</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Urological surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McGarry, Sean D.</creatorcontrib><creatorcontrib>Brehler, Michael</creatorcontrib><creatorcontrib>Bukowy, John D.</creatorcontrib><creatorcontrib>Lowman, Allison K.</creatorcontrib><creatorcontrib>Bobholz, Samuel A.</creatorcontrib><creatorcontrib>Duenweg, Savannah R.</creatorcontrib><creatorcontrib>Banerjee, Anjishnu</creatorcontrib><creatorcontrib>Hurrell, Sarah L.</creatorcontrib><creatorcontrib>Malyarenko, Dariya</creatorcontrib><creatorcontrib>Chenevert, Thomas L.</creatorcontrib><creatorcontrib>Cao, Yue</creatorcontrib><creatorcontrib>Li, Yuan</creatorcontrib><creatorcontrib>You, Daekeun</creatorcontrib><creatorcontrib>Fedorov, Andrey</creatorcontrib><creatorcontrib>Bell, Laura C.</creatorcontrib><creatorcontrib>Quarles, C. Chad</creatorcontrib><creatorcontrib>Prah, Melissa A.</creatorcontrib><creatorcontrib>Schmainda, Kathleen M.</creatorcontrib><creatorcontrib>Taouli, Bachir</creatorcontrib><creatorcontrib>LoCastro, Eve</creatorcontrib><creatorcontrib>Mazaheri, Yousef</creatorcontrib><creatorcontrib>Shukla‐Dave, Amita</creatorcontrib><creatorcontrib>Yankeelov, Thomas E.</creatorcontrib><creatorcontrib>Hormuth, David A.</creatorcontrib><creatorcontrib>Madhuranthakam, Ananth J.</creatorcontrib><creatorcontrib>Hulsey, Keith</creatorcontrib><creatorcontrib>Li, Kurt</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Muzi, Mark</creatorcontrib><creatorcontrib>Jacobs, Michael A.</creatorcontrib><creatorcontrib>Solaiyappan, Meiyappan</creatorcontrib><creatorcontrib>Hectors, Stefanie</creatorcontrib><creatorcontrib>Antic, Tatjana</creatorcontrib><creatorcontrib>Paner, Gladell P.</creatorcontrib><creatorcontrib>Palangmonthip, Watchareepohn</creatorcontrib><creatorcontrib>Jacobsohn, Kenneth</creatorcontrib><creatorcontrib>Hohenwalter, Mark</creatorcontrib><creatorcontrib>Duvnjak, Petar</creatorcontrib><creatorcontrib>Griffin, Michael</creatorcontrib><creatorcontrib>See, William</creatorcontrib><creatorcontrib>Nevalainen, Marja T.</creatorcontrib><creatorcontrib>Iczkowski, Kenneth A.</creatorcontrib><creatorcontrib>LaViolette, Peter S.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Free Content</collection><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>McGarry, Sean D.</au><au>Brehler, Michael</au><au>Bukowy, John D.</au><au>Lowman, Allison K.</au><au>Bobholz, Samuel A.</au><au>Duenweg, Savannah R.</au><au>Banerjee, Anjishnu</au><au>Hurrell, Sarah L.</au><au>Malyarenko, Dariya</au><au>Chenevert, Thomas L.</au><au>Cao, Yue</au><au>Li, Yuan</au><au>You, Daekeun</au><au>Fedorov, Andrey</au><au>Bell, Laura C.</au><au>Quarles, C. Chad</au><au>Prah, Melissa A.</au><au>Schmainda, Kathleen M.</au><au>Taouli, Bachir</au><au>LoCastro, Eve</au><au>Mazaheri, Yousef</au><au>Shukla‐Dave, Amita</au><au>Yankeelov, Thomas E.</au><au>Hormuth, David A.</au><au>Madhuranthakam, Ananth J.</au><au>Hulsey, Keith</au><au>Li, Kurt</au><au>Huang, Wei</au><au>Huang, Wei</au><au>Muzi, Mark</au><au>Jacobs, Michael A.</au><au>Solaiyappan, Meiyappan</au><au>Hectors, Stefanie</au><au>Antic, Tatjana</au><au>Paner, Gladell P.</au><au>Palangmonthip, Watchareepohn</au><au>Jacobsohn, Kenneth</au><au>Hohenwalter, Mark</au><au>Duvnjak, Petar</au><au>Griffin, Michael</au><au>See, William</au><au>Nevalainen, Marja T.</au><au>Iczkowski, Kenneth A.</au><au>LaViolette, Peter S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2022-06</date><risdate>2022</risdate><volume>55</volume><issue>6</issue><spage>1745</spage><epage>1758</epage><pages>1745-1758</pages><issn>1053-1807</issn><issn>1522-2586</issn><eissn>1522-2586</eissn><abstract>Background
Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.
Purpose
To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.
Study Type
Prospective.
Population
Thirty‐three patients prospectively imaged prior to prostatectomy.
Field Strength/Sequence
3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence.
Assessment
Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC).
Statistical Test
Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant.
Results
The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size.
Data Conclusion
We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer.
Level of Evidence
1
Technical Efficacy
Stage 3</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>34767682</pmid><doi>10.1002/jmri.27983</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-3975-8153</orcidid><orcidid>https://orcid.org/0000-0002-9602-6891</orcidid><orcidid>https://orcid.org/0000-0001-7456-3197</orcidid><orcidid>https://orcid.org/0000-0001-6409-1333</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1053-1807 |
| ispartof | Journal of magnetic resonance imaging, 2022-06, Vol.55 (6), p.1745-1758 |
| issn | 1053-1807 1522-2586 1522-2586 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2597491136 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms cancer Cancer surgery Datasets Differentiation Diffusion Diffusion coefficient Diffusion Magnetic Resonance Imaging - methods Discordance Field strength Humans Kurtosis Magnetic resonance imaging Male Medical imaging MRI multisite |modelling Perfusion Population studies Process parameters Prospective Studies prostate Prostate cancer Prostatectomy Prostatic Neoplasms - diagnostic imaging Retrospective Studies ROC Curve Sensitivity and Specificity Statistical analysis Statistical tests Urological surgery |
| title | Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness |
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