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Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy
•Multiparametric MRI was helpful in differentiating HGGs from LGGs.•Relative CBV is the most helpful parameter in differentiating HGGs from LGGs.•Ktrans may aid in the prediction of the IDH mutational status of diffuse gliomas.•Relative CBV showed significant correlation with Ktrans, Cho/Cr, and ADC...
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Published in: | European journal of radiology 2023-08, Vol.165, p.110888-110888, Article 110888 |
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creator | Seo, Minkook Choi, Yangsean Soo Lee, Youn Ahn, Kook-Jin Kim, Bum-soo Park, Jae-Sung Jeon, Sin-Soo |
description | •Multiparametric MRI was helpful in differentiating HGGs from LGGs.•Relative CBV is the most helpful parameter in differentiating HGGs from LGGs.•Ktrans may aid in the prediction of the IDH mutational status of diffuse gliomas.•Relative CBV showed significant correlation with Ktrans, Cho/Cr, and ADC.•Some HGGs had overlapping features in Ktrans and rCBV with LGGs.
To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).
Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed.
HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P |
doi_str_mv | 10.1016/j.ejrad.2023.110888 |
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To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).
Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed.
HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67–0.91) and 0.74 (95% CI, 0.59–0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48–0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52–0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57–0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01).
The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.</description><identifier>ISSN: 0720-048X</identifier><identifier>EISSN: 1872-7727</identifier><identifier>DOI: 10.1016/j.ejrad.2023.110888</identifier><identifier>PMID: 37257338</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Glioblastoma ; Glioma ; Multiparametric Magnetic Resonance Imaging</subject><ispartof>European journal of radiology, 2023-08, Vol.165, p.110888-110888, Article 110888</ispartof><rights>2023 Elsevier B.V.</rights><rights>Copyright © 2023 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-c9f4e507bd8c8d57b780a30bb504a8cf9ea95a903f06a50faa439b40bcb64f723</citedby><cites>FETCH-LOGICAL-c359t-c9f4e507bd8c8d57b780a30bb504a8cf9ea95a903f06a50faa439b40bcb64f723</cites><orcidid>0000-0003-1674-7101</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37257338$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Seo, Minkook</creatorcontrib><creatorcontrib>Choi, Yangsean</creatorcontrib><creatorcontrib>Soo Lee, Youn</creatorcontrib><creatorcontrib>Ahn, Kook-Jin</creatorcontrib><creatorcontrib>Kim, Bum-soo</creatorcontrib><creatorcontrib>Park, Jae-Sung</creatorcontrib><creatorcontrib>Jeon, Sin-Soo</creatorcontrib><title>Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>•Multiparametric MRI was helpful in differentiating HGGs from LGGs.•Relative CBV is the most helpful parameter in differentiating HGGs from LGGs.•Ktrans may aid in the prediction of the IDH mutational status of diffuse gliomas.•Relative CBV showed significant correlation with Ktrans, Cho/Cr, and ADC.•Some HGGs had overlapping features in Ktrans and rCBV with LGGs.
To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).
Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed.
HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67–0.91) and 0.74 (95% CI, 0.59–0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48–0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52–0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57–0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01).
The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.</description><subject>Glioblastoma</subject><subject>Glioma</subject><subject>Multiparametric Magnetic Resonance Imaging</subject><issn>0720-048X</issn><issn>1872-7727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kcFu1DAQhi0EokvhCZBQjhyaxbGTtYPEAVW0VCpCQiBxsyb2ZNer2A62A9oH4_3qZdseuXgkzzfzz8xPyOuGrhvabN7t17iPYNaMMr5uGiqlfEJWjRSsFoKJp2RFBaM1beXPM_IipT2ltGt79pycccE6wblckb_Xkw0Oqm3pZP22WtLxdcuU7QwRHOZodfXl2837aodg6hzqY6x0cCVvU_AVuFBKzMGDK2haksY528FONh8K53OElC8egYefGv0OvEaH_pi141i0g6__oN3uMprKOthiuqjAmzJAlWbUOYakw3x4SZ6NMCV8dR_PyY-rT98vP9e3X69vLj_e1pp3fa51P7bYUTEYqaXpxCAkBU6HoaMtSD32CH0HPeUj3UBHR4CW90NLBz1s2lEwfk7envrOMfxaMGXlbFlvmsBjWJJikjUb3kshCspPqC4zpoijmmPZIB5UQ9XRL7VX__xSR7_Uya9S9eZeYBkcmseaB4MK8OEEYFnzt8WokrZYzmZsLPdQJtj_CtwB0LGs-A</recordid><startdate>202308</startdate><enddate>202308</enddate><creator>Seo, Minkook</creator><creator>Choi, Yangsean</creator><creator>Soo Lee, Youn</creator><creator>Ahn, Kook-Jin</creator><creator>Kim, Bum-soo</creator><creator>Park, Jae-Sung</creator><creator>Jeon, Sin-Soo</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1674-7101</orcidid></search><sort><creationdate>202308</creationdate><title>Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy</title><author>Seo, Minkook ; Choi, Yangsean ; Soo Lee, Youn ; Ahn, Kook-Jin ; Kim, Bum-soo ; Park, Jae-Sung ; Jeon, Sin-Soo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-c9f4e507bd8c8d57b780a30bb504a8cf9ea95a903f06a50faa439b40bcb64f723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Glioblastoma</topic><topic>Glioma</topic><topic>Multiparametric Magnetic Resonance Imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Seo, Minkook</creatorcontrib><creatorcontrib>Choi, Yangsean</creatorcontrib><creatorcontrib>Soo Lee, Youn</creatorcontrib><creatorcontrib>Ahn, Kook-Jin</creatorcontrib><creatorcontrib>Kim, Bum-soo</creatorcontrib><creatorcontrib>Park, Jae-Sung</creatorcontrib><creatorcontrib>Jeon, Sin-Soo</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Seo, Minkook</au><au>Choi, Yangsean</au><au>Soo Lee, Youn</au><au>Ahn, Kook-Jin</au><au>Kim, Bum-soo</au><au>Park, Jae-Sung</au><au>Jeon, Sin-Soo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2023-08</date><risdate>2023</risdate><volume>165</volume><spage>110888</spage><epage>110888</epage><pages>110888-110888</pages><artnum>110888</artnum><issn>0720-048X</issn><eissn>1872-7727</eissn><abstract>•Multiparametric MRI was helpful in differentiating HGGs from LGGs.•Relative CBV is the most helpful parameter in differentiating HGGs from LGGs.•Ktrans may aid in the prediction of the IDH mutational status of diffuse gliomas.•Relative CBV showed significant correlation with Ktrans, Cho/Cr, and ADC.•Some HGGs had overlapping features in Ktrans and rCBV with LGGs.
To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).
Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman’s correlation coefficients (ρ) were computed.
HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67–0.91) and 0.74 (95% CI, 0.59–0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48–0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52–0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57–0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01).
The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>37257338</pmid><doi>10.1016/j.ejrad.2023.110888</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-1674-7101</orcidid></addata></record> |
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subjects | Glioblastoma Glioma Multiparametric Magnetic Resonance Imaging |
title | Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy |
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