Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics

Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p...
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Autor*in:	Bahrami, Naeim [verfasserIn] Hartman, Stephen J. [verfasserIn] Chang, Yu-Hsuan [verfasserIn] Delfanti, Rachel [verfasserIn] White, Nathan S. [verfasserIn] Karunamuni, Roshan [verfasserIn] Seibert, Tyler M. [verfasserIn] Dale, Anders M. [verfasserIn] Hattangadi-Gluth, Jona A. [verfasserIn] Piccioni, David [verfasserIn] Farid, Nikdokht [verfasserIn] McDonald, Carrie R. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Grade II/III gliomas Texture analysis IDH 1p/19q Magnetic resonance imaging

Übergeordnetes Werk:	Enthalten in: Journal of neuro-oncology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983, 139(2018), 3 vom: 02. Juni, Seite 633-642
Übergeordnetes Werk:	volume:139 ; year:2018 ; number:3 ; day:02 ; month:06 ; pages:633-642

Links:	Volltext

DOI / URN:	10.1007/s11060-018-2908-3

Katalog-ID:	SPR016193326

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245	1	0	\|a Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
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520			\|a Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
650		4	\|a Grade II/III gliomas \|7 (dpeaa)DE-He213
650		4	\|a Texture analysis \|7 (dpeaa)DE-He213
650		4	\|a IDH \|7 (dpeaa)DE-He213
650		4	\|a 1p/19q \|7 (dpeaa)DE-He213
650		4	\|a Magnetic resonance imaging \|7 (dpeaa)DE-He213
700	1		\|a Hartman, Stephen J. \|e verfasserin \|4 aut
700	1		\|a Chang, Yu-Hsuan \|e verfasserin \|4 aut
700	1		\|a Delfanti, Rachel \|e verfasserin \|4 aut
700	1		\|a White, Nathan S. \|e verfasserin \|4 aut
700	1		\|a Karunamuni, Roshan \|e verfasserin \|4 aut
700	1		\|a Seibert, Tyler M. \|e verfasserin \|4 aut
700	1		\|a Dale, Anders M. \|e verfasserin \|4 aut
700	1		\|a Hattangadi-Gluth, Jona A. \|e verfasserin \|4 aut
700	1		\|a Piccioni, David \|e verfasserin \|4 aut
700	1		\|a Farid, Nikdokht \|e verfasserin \|4 aut
700	1		\|a McDonald, Carrie R. \|e verfasserin \|4 aut
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Indexfelder

author_variant	n b nb s j h sj sjh y h c yhc r d rd n s w ns nsw r k rk t m s tm tms a m d am amd j a h g jah jahg d p dp n f nf c r m cr crm
matchkey_str	article:15737373:2018----::oeuacasfctooptetwtgaeiigimuigunia
hierarchy_sort_str	2018
bklnumber	44.81 44.90
publishDate	2018
allfields	10.1007/s11060-018-2908-3 doi (DE-627)SPR016193326 (SPR)s11060-018-2908-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Bahrami, Naeim verfasserin aut Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management. Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Hartman, Stephen J. verfasserin aut Chang, Yu-Hsuan verfasserin aut Delfanti, Rachel verfasserin aut White, Nathan S. verfasserin aut Karunamuni, Roshan verfasserin aut Seibert, Tyler M. verfasserin aut Dale, Anders M. verfasserin aut Hattangadi-Gluth, Jona A. verfasserin aut Piccioni, David verfasserin aut Farid, Nikdokht verfasserin aut McDonald, Carrie R. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 139(2018), 3 vom: 02. Juni, Seite 633-642 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:139 year:2018 number:3 day:02 month:06 pages:633-642 https://dx.doi.org/10.1007/s11060-018-2908-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE 44.90 ASE AR 139 2018 3 02 06 633-642
spelling	10.1007/s11060-018-2908-3 doi (DE-627)SPR016193326 (SPR)s11060-018-2908-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Bahrami, Naeim verfasserin aut Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management. Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Hartman, Stephen J. verfasserin aut Chang, Yu-Hsuan verfasserin aut Delfanti, Rachel verfasserin aut White, Nathan S. verfasserin aut Karunamuni, Roshan verfasserin aut Seibert, Tyler M. verfasserin aut Dale, Anders M. verfasserin aut Hattangadi-Gluth, Jona A. verfasserin aut Piccioni, David verfasserin aut Farid, Nikdokht verfasserin aut McDonald, Carrie R. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 139(2018), 3 vom: 02. Juni, Seite 633-642 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:139 year:2018 number:3 day:02 month:06 pages:633-642 https://dx.doi.org/10.1007/s11060-018-2908-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE 44.90 ASE AR 139 2018 3 02 06 633-642
allfields_unstemmed	10.1007/s11060-018-2908-3 doi (DE-627)SPR016193326 (SPR)s11060-018-2908-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Bahrami, Naeim verfasserin aut Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management. Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Hartman, Stephen J. verfasserin aut Chang, Yu-Hsuan verfasserin aut Delfanti, Rachel verfasserin aut White, Nathan S. verfasserin aut Karunamuni, Roshan verfasserin aut Seibert, Tyler M. verfasserin aut Dale, Anders M. verfasserin aut Hattangadi-Gluth, Jona A. verfasserin aut Piccioni, David verfasserin aut Farid, Nikdokht verfasserin aut McDonald, Carrie R. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 139(2018), 3 vom: 02. Juni, Seite 633-642 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:139 year:2018 number:3 day:02 month:06 pages:633-642 https://dx.doi.org/10.1007/s11060-018-2908-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE 44.90 ASE AR 139 2018 3 02 06 633-642
allfieldsGer	10.1007/s11060-018-2908-3 doi (DE-627)SPR016193326 (SPR)s11060-018-2908-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Bahrami, Naeim verfasserin aut Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management. Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Hartman, Stephen J. verfasserin aut Chang, Yu-Hsuan verfasserin aut Delfanti, Rachel verfasserin aut White, Nathan S. verfasserin aut Karunamuni, Roshan verfasserin aut Seibert, Tyler M. verfasserin aut Dale, Anders M. verfasserin aut Hattangadi-Gluth, Jona A. verfasserin aut Piccioni, David verfasserin aut Farid, Nikdokht verfasserin aut McDonald, Carrie R. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 139(2018), 3 vom: 02. Juni, Seite 633-642 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:139 year:2018 number:3 day:02 month:06 pages:633-642 https://dx.doi.org/10.1007/s11060-018-2908-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE 44.90 ASE AR 139 2018 3 02 06 633-642
allfieldsSound	10.1007/s11060-018-2908-3 doi (DE-627)SPR016193326 (SPR)s11060-018-2908-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Bahrami, Naeim verfasserin aut Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management. Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Hartman, Stephen J. verfasserin aut Chang, Yu-Hsuan verfasserin aut Delfanti, Rachel verfasserin aut White, Nathan S. verfasserin aut Karunamuni, Roshan verfasserin aut Seibert, Tyler M. verfasserin aut Dale, Anders M. verfasserin aut Hattangadi-Gluth, Jona A. verfasserin aut Piccioni, David verfasserin aut Farid, Nikdokht verfasserin aut McDonald, Carrie R. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 139(2018), 3 vom: 02. Juni, Seite 633-642 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:139 year:2018 number:3 day:02 month:06 pages:633-642 https://dx.doi.org/10.1007/s11060-018-2908-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE 44.90 ASE AR 139 2018 3 02 06 633-642
language	English
source	Enthalten in Journal of neuro-oncology 139(2018), 3 vom: 02. Juni, Seite 633-642 volume:139 year:2018 number:3 day:02 month:06 pages:633-642
sourceStr	Enthalten in Journal of neuro-oncology 139(2018), 3 vom: 02. Juni, Seite 633-642 volume:139 year:2018 number:3 day:02 month:06 pages:633-642
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Grade II/III gliomas Texture analysis IDH 1p/19q Magnetic resonance imaging
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of neuro-oncology
authorswithroles_txt_mv	Bahrami, Naeim @@aut@@ Hartman, Stephen J. @@aut@@ Chang, Yu-Hsuan @@aut@@ Delfanti, Rachel @@aut@@ White, Nathan S. @@aut@@ Karunamuni, Roshan @@aut@@ Seibert, Tyler M. @@aut@@ Dale, Anders M. @@aut@@ Hattangadi-Gluth, Jona A. @@aut@@ Piccioni, David @@aut@@ Farid, Nikdokht @@aut@@ McDonald, Carrie R. @@aut@@
publishDateDaySort_date	2018-06-02T00:00:00Z
hierarchy_top_id	32046122X
dewey-sort	3610
id	SPR016193326
language_de	englisch
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The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Grade II/III gliomas</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IDH</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">1p/19q</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic resonance imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hartman, Stephen J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Yu-Hsuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Delfanti, Rachel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">White, Nathan S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Karunamuni, Roshan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Seibert, Tyler M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dale, Anders M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hattangadi-Gluth, Jona A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Piccioni, David</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Farid, Nikdokht</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">McDonald, Carrie R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of neuro-oncology</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983</subfield><subfield code="g">139(2018), 3 vom: 02. 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author	Bahrami, Naeim
spellingShingle	Bahrami, Naeim ddc 610 bkl 44.81 bkl 44.90 misc Grade II/III gliomas misc Texture analysis misc IDH misc 1p/19q misc Magnetic resonance imaging Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
authorStr	Bahrami, Naeim
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topic_title	610 ASE 44.81 bkl 44.90 bkl Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics Grade II/III gliomas (dpeaa)DE-He213 Texture analysis (dpeaa)DE-He213 IDH (dpeaa)DE-He213 1p/19q (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213
topic	ddc 610 bkl 44.81 bkl 44.90 misc Grade II/III gliomas misc Texture analysis misc IDH misc 1p/19q misc Magnetic resonance imaging
topic_unstemmed	ddc 610 bkl 44.81 bkl 44.90 misc Grade II/III gliomas misc Texture analysis misc IDH misc 1p/19q misc Magnetic resonance imaging
topic_browse	ddc 610 bkl 44.81 bkl 44.90 misc Grade II/III gliomas misc Texture analysis misc IDH misc 1p/19q misc Magnetic resonance imaging
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hierarchy_parent_title	Journal of neuro-oncology
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title	Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
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title_full	Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
author_sort	Bahrami, Naeim
journal	Journal of neuro-oncology
journalStr	Journal of neuro-oncology
lang_code	eng
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author_browse	Bahrami, Naeim Hartman, Stephen J. Chang, Yu-Hsuan Delfanti, Rachel White, Nathan S. Karunamuni, Roshan Seibert, Tyler M. Dale, Anders M. Hattangadi-Gluth, Jona A. Piccioni, David Farid, Nikdokht McDonald, Carrie R.
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format_se	Elektronische Aufsätze
author-letter	Bahrami, Naeim
doi_str_mv	10.1007/s11060-018-2908-3
dewey-full	610
author2-role	verfasserin
title_sort	molecular classification of patients with grade ii/iii glioma using quantitative mri characteristics
title_auth	Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
abstract	Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
abstractGer	Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
abstract_unstemmed	Background Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Conclusion Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
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container_issue	3
title_short	Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics
url	https://dx.doi.org/10.1007/s11060-018-2908-3
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author2	Hartman, Stephen J. Chang, Yu-Hsuan Delfanti, Rachel White, Nathan S. Karunamuni, Roshan Seibert, Tyler M. Dale, Anders M. Hattangadi-Gluth, Jona A. Piccioni, David Farid, Nikdokht McDonald, Carrie R.
author2Str	Hartman, Stephen J. Chang, Yu-Hsuan Delfanti, Rachel White, Nathan S. Karunamuni, Roshan Seibert, Tyler M. Dale, Anders M. Hattangadi-Gluth, Jona A. Piccioni, David Farid, Nikdokht McDonald, Carrie R.
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doi_str	10.1007/s11060-018-2908-3
up_date	2024-07-03T21:28:25.939Z
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The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging. Methods Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Results Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. 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Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics

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