Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance

Abstract Purpose To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions. Materials and methods 66...

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Veröffentlicht in:Magnetic resonance imaging 2016-05, Vol.34 (4), p.496-501
Hauptverfasser: Othman, Ahmed E, Falkner, Florian, Kessler, David-Emanuel, Martirosian, Petros, Weiss, Jakob, Kruck, Stephan, Kaufmann, Sascha, Grimm, Robert, Kramer, Ulrich, Nikolaou, Konstantin, Notohamiprodjo, Mike
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container_end_page 501
container_issue 4
container_start_page 496
container_title Magnetic resonance imaging
container_volume 34
creator Othman, Ahmed E
Falkner, Florian
Kessler, David-Emanuel
Martirosian, Petros
Weiss, Jakob
Kruck, Stephan
Kaufmann, Sascha
Grimm, Robert
Kramer, Ulrich
Nikolaou, Konstantin
Notohamiprodjo, Mike
description Abstract Purpose To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions. Materials and methods 66 male patients (age 65.4 ± 10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3 T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤ 3, n = 32; B: PI-RADS > 3, n = 34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi2 ) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs. Results Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p < .001). Ktrans and Kep were significantly higher in group B compared to group A (all p < .001). For chi2 , lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi2 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve. Conclusion Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.
doi_str_mv 10.1016/j.mri.2015.12.009
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Materials and methods 66 male patients (age 65.4 ± 10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3 T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤ 3, n = 32; B: PI-RADS &gt; 3, n = 34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi2 ) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs. Results Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p &lt; .001). Ktrans and Kep were significantly higher in group B compared to group A (all p &lt; .001). For chi2 , lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi2 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve. Conclusion Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.</description><identifier>ISSN: 0730-725X</identifier><identifier>EISSN: 1873-5894</identifier><identifier>DOI: 10.1016/j.mri.2015.12.009</identifier><identifier>PMID: 26708031</identifier><language>eng</language><publisher>Netherlands: Elsevier Inc</publisher><subject>Aged ; Arterial-input-function ; Contrast Media - pharmacokinetics ; DCE-MRI ; Dynamic imaging ; Humans ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; Models, Theoretical ; Prostate ; Prostate - diagnostic imaging ; Prostate cancer ; Prostatic Neoplasms - diagnostic imaging ; Radiology ; Retrospective Studies ; Software</subject><ispartof>Magnetic resonance imaging, 2016-05, Vol.34 (4), p.496-501</ispartof><rights>Elsevier Inc.</rights><rights>2015 Elsevier Inc.</rights><rights>Copyright © 2015 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-8098d5f7a8f9e2a41f0d4a2d7ff5f3b962b7c263e9479bcf5b5c2cb4630df8903</citedby><cites>FETCH-LOGICAL-c441t-8098d5f7a8f9e2a41f0d4a2d7ff5f3b962b7c263e9479bcf5b5c2cb4630df8903</cites><orcidid>0000-0002-3827-8695 ; 0000-0002-4283-3890</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.mri.2015.12.009$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26708031$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Othman, Ahmed E</creatorcontrib><creatorcontrib>Falkner, Florian</creatorcontrib><creatorcontrib>Kessler, David-Emanuel</creatorcontrib><creatorcontrib>Martirosian, Petros</creatorcontrib><creatorcontrib>Weiss, Jakob</creatorcontrib><creatorcontrib>Kruck, Stephan</creatorcontrib><creatorcontrib>Kaufmann, Sascha</creatorcontrib><creatorcontrib>Grimm, Robert</creatorcontrib><creatorcontrib>Kramer, Ulrich</creatorcontrib><creatorcontrib>Nikolaou, Konstantin</creatorcontrib><creatorcontrib>Notohamiprodjo, Mike</creatorcontrib><title>Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance</title><title>Magnetic resonance imaging</title><addtitle>Magn Reson Imaging</addtitle><description>Abstract Purpose To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions. Materials and methods 66 male patients (age 65.4 ± 10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3 T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤ 3, n = 32; B: PI-RADS &gt; 3, n = 34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi2 ) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs. Results Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p &lt; .001). Ktrans and Kep were significantly higher in group B compared to group A (all p &lt; .001). For chi2 , lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi2 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve. Conclusion Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. 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Falkner, Florian ; Kessler, David-Emanuel ; Martirosian, Petros ; Weiss, Jakob ; Kruck, Stephan ; Kaufmann, Sascha ; Grimm, Robert ; Kramer, Ulrich ; Nikolaou, Konstantin ; Notohamiprodjo, Mike</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-8098d5f7a8f9e2a41f0d4a2d7ff5f3b962b7c263e9479bcf5b5c2cb4630df8903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Aged</topic><topic>Arterial-input-function</topic><topic>Contrast Media - pharmacokinetics</topic><topic>DCE-MRI</topic><topic>Dynamic imaging</topic><topic>Humans</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Models, Theoretical</topic><topic>Prostate</topic><topic>Prostate - diagnostic imaging</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - diagnostic imaging</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Othman, Ahmed E</creatorcontrib><creatorcontrib>Falkner, Florian</creatorcontrib><creatorcontrib>Kessler, David-Emanuel</creatorcontrib><creatorcontrib>Martirosian, Petros</creatorcontrib><creatorcontrib>Weiss, Jakob</creatorcontrib><creatorcontrib>Kruck, Stephan</creatorcontrib><creatorcontrib>Kaufmann, Sascha</creatorcontrib><creatorcontrib>Grimm, Robert</creatorcontrib><creatorcontrib>Kramer, Ulrich</creatorcontrib><creatorcontrib>Nikolaou, Konstantin</creatorcontrib><creatorcontrib>Notohamiprodjo, Mike</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Othman, Ahmed E</au><au>Falkner, Florian</au><au>Kessler, David-Emanuel</au><au>Martirosian, Petros</au><au>Weiss, Jakob</au><au>Kruck, Stephan</au><au>Kaufmann, Sascha</au><au>Grimm, Robert</au><au>Kramer, Ulrich</au><au>Nikolaou, Konstantin</au><au>Notohamiprodjo, Mike</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance</atitle><jtitle>Magnetic resonance imaging</jtitle><addtitle>Magn Reson Imaging</addtitle><date>2016-05-01</date><risdate>2016</risdate><volume>34</volume><issue>4</issue><spage>496</spage><epage>501</epage><pages>496-501</pages><issn>0730-725X</issn><eissn>1873-5894</eissn><abstract>Abstract Purpose To assess the effect of different population-averaged arterial-input-functions (pAIF) on pharmacokinetic parameters from dynamic contrast-enhanced MRI (DCE-MRI) and their diagnostic accuracy regarding the detection of potentially malignant prostate lesions. Materials and methods 66 male patients (age 65.4 ± 10.8y) with suspected prostate cancer underwent multiparametric MRI of the prostate including T2-w, DWI-w and DCE-MRI sequences at a 3 T MRI scanner. All detected lesions were categorized based on ACR PI-RADS version 2 and divided into 2 groups (A: PI-RADS ≤ 3, n = 32; B: PI-RADS &gt; 3, n = 34). In each DCE-MRI dataset, pharmacokinetic parameters (Ktrans, Kep and ve) and goodness of fit (chi2 ) were generated using the Tofts model with 3 different pAIFs (fast, intermediate, slow) as provided by a commercially available postprocessing software. Pharmacokinetic parameters, their diagnostic accuracies and model fits were compared for the 3 pAIFs. Results Ktrans, Kep and ve differed significantly among the 3 pAIFs (all p &lt; .001). Ktrans and Kep were significantly higher in group B compared to group A (all p &lt; .001). For chi2 , lowest results (representing highest goodness of fit) were found for intermediate pAIF (chi2 0.073). ROC analyses revealed comparable diagnostic accuracies for the different pAIFs, which were high for Ktrans and Kep and low for ve. Conclusion Choosing various pAIF types causes a high variability in pharmacokinetic parameter estimates. Therefore, it is of great importance to consider this as potential artifact and thus keep AIF type selection constant in DCE-MRI studies.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>26708031</pmid><doi>10.1016/j.mri.2015.12.009</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-3827-8695</orcidid><orcidid>https://orcid.org/0000-0002-4283-3890</orcidid></addata></record>
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subjects Aged
Arterial-input-function
Contrast Media - pharmacokinetics
DCE-MRI
Dynamic imaging
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Middle Aged
Models, Theoretical
Prostate
Prostate - diagnostic imaging
Prostate cancer
Prostatic Neoplasms - diagnostic imaging
Radiology
Retrospective Studies
Software
title Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance
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