Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign
Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high app...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Aliotta, Eric [verfasserIn] Dutta, Sunil W. [verfasserIn] Feng, Xue [verfasserIn] Tustison, Nicholas J. [verfasserIn] Batchala, Prem P. [verfasserIn] Schiff, David [verfasserIn] Lopes, M. Beatriz [verfasserIn] Jain, Rajan [verfasserIn] Druzgal, T. Jason [verfasserIn] Mukherjee, Sugoto [verfasserIn] Patel, Sohil H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of neuro-oncology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983, 149(2020), 2 vom: Sept., Seite 325-335 |
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| Übergeordnetes Werk: |
volume:149 ; year:2020 ; number:2 ; month:09 ; pages:325-335 |
| Links: |
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| DOI / URN: |
10.1007/s11060-020-03611-8 |
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| Katalog-ID: |
SPR041237803 |
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| 024 | 7 | |a 10.1007/s11060-020-03611-8 |2 doi | |
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| 100 | 1 | |a Aliotta, Eric |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. | ||
| 650 | 4 | |a Lower grade glioma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Diffusion weighted imaging |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Genetic classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a IDH mutation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Dutta, Sunil W. |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Xue |e verfasserin |4 aut | |
| 700 | 1 | |a Tustison, Nicholas J. |e verfasserin |4 aut | |
| 700 | 1 | |a Batchala, Prem P. |e verfasserin |4 aut | |
| 700 | 1 | |a Schiff, David |e verfasserin |4 aut | |
| 700 | 1 | |a Lopes, M. Beatriz |e verfasserin |4 aut | |
| 700 | 1 | |a Jain, Rajan |e verfasserin |4 aut | |
| 700 | 1 | |a Druzgal, T. Jason |e verfasserin |4 aut | |
| 700 | 1 | |a Mukherjee, Sugoto |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Sohil H. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of neuro-oncology |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 |g 149(2020), 2 vom: Sept., Seite 325-335 |w (DE-627)32046122X |w (DE-600)2007293-4 |x 1573-7373 |7 nnns |
| 773 | 1 | 8 | |g volume:149 |g year:2020 |g number:2 |g month:09 |g pages:325-335 |
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10.1007/s11060-020-03611-8 doi (DE-627)SPR041237803 (SPR)s11060-020-03611-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Aliotta, Eric verfasserin aut Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 Dutta, Sunil W. verfasserin aut Feng, Xue verfasserin aut Tustison, Nicholas J. verfasserin aut Batchala, Prem P. verfasserin aut Schiff, David verfasserin aut Lopes, M. Beatriz verfasserin aut Jain, Rajan verfasserin aut Druzgal, T. Jason verfasserin aut Mukherjee, Sugoto verfasserin aut Patel, Sohil H. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 149(2020), 2 vom: Sept., Seite 325-335 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:149 year:2020 number:2 month:09 pages:325-335 https://dx.doi.org/10.1007/s11060-020-03611-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 149 2020 2 09 325-335 |
| spelling |
10.1007/s11060-020-03611-8 doi (DE-627)SPR041237803 (SPR)s11060-020-03611-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Aliotta, Eric verfasserin aut Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 Dutta, Sunil W. verfasserin aut Feng, Xue verfasserin aut Tustison, Nicholas J. verfasserin aut Batchala, Prem P. verfasserin aut Schiff, David verfasserin aut Lopes, M. Beatriz verfasserin aut Jain, Rajan verfasserin aut Druzgal, T. Jason verfasserin aut Mukherjee, Sugoto verfasserin aut Patel, Sohil H. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 149(2020), 2 vom: Sept., Seite 325-335 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:149 year:2020 number:2 month:09 pages:325-335 https://dx.doi.org/10.1007/s11060-020-03611-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 149 2020 2 09 325-335 |
| allfields_unstemmed |
10.1007/s11060-020-03611-8 doi (DE-627)SPR041237803 (SPR)s11060-020-03611-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Aliotta, Eric verfasserin aut Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 Dutta, Sunil W. verfasserin aut Feng, Xue verfasserin aut Tustison, Nicholas J. verfasserin aut Batchala, Prem P. verfasserin aut Schiff, David verfasserin aut Lopes, M. Beatriz verfasserin aut Jain, Rajan verfasserin aut Druzgal, T. Jason verfasserin aut Mukherjee, Sugoto verfasserin aut Patel, Sohil H. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 149(2020), 2 vom: Sept., Seite 325-335 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:149 year:2020 number:2 month:09 pages:325-335 https://dx.doi.org/10.1007/s11060-020-03611-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 149 2020 2 09 325-335 |
| allfieldsGer |
10.1007/s11060-020-03611-8 doi (DE-627)SPR041237803 (SPR)s11060-020-03611-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Aliotta, Eric verfasserin aut Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 Dutta, Sunil W. verfasserin aut Feng, Xue verfasserin aut Tustison, Nicholas J. verfasserin aut Batchala, Prem P. verfasserin aut Schiff, David verfasserin aut Lopes, M. Beatriz verfasserin aut Jain, Rajan verfasserin aut Druzgal, T. Jason verfasserin aut Mukherjee, Sugoto verfasserin aut Patel, Sohil H. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 149(2020), 2 vom: Sept., Seite 325-335 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:149 year:2020 number:2 month:09 pages:325-335 https://dx.doi.org/10.1007/s11060-020-03611-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 149 2020 2 09 325-335 |
| allfieldsSound |
10.1007/s11060-020-03611-8 doi (DE-627)SPR041237803 (SPR)s11060-020-03611-8-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl 44.90 bkl Aliotta, Eric verfasserin aut Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 Dutta, Sunil W. verfasserin aut Feng, Xue verfasserin aut Tustison, Nicholas J. verfasserin aut Batchala, Prem P. verfasserin aut Schiff, David verfasserin aut Lopes, M. Beatriz verfasserin aut Jain, Rajan verfasserin aut Druzgal, T. Jason verfasserin aut Mukherjee, Sugoto verfasserin aut Patel, Sohil H. verfasserin aut Enthalten in Journal of neuro-oncology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1983 149(2020), 2 vom: Sept., Seite 325-335 (DE-627)32046122X (DE-600)2007293-4 1573-7373 nnns volume:149 year:2020 number:2 month:09 pages:325-335 https://dx.doi.org/10.1007/s11060-020-03611-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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 149 2020 2 09 325-335 |
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Enthalten in Journal of neuro-oncology 149(2020), 2 vom: Sept., Seite 325-335 volume:149 year:2020 number:2 month:09 pages:325-335 |
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Lower grade glioma Diffusion weighted imaging Genetic classification IDH mutation |
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Journal of neuro-oncology |
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Aliotta, Eric @@aut@@ Dutta, Sunil W. @@aut@@ Feng, Xue @@aut@@ Tustison, Nicholas J. @@aut@@ Batchala, Prem P. @@aut@@ Schiff, David @@aut@@ Lopes, M. Beatriz @@aut@@ Jain, Rajan @@aut@@ Druzgal, T. Jason @@aut@@ Mukherjee, Sugoto @@aut@@ Patel, Sohil H. @@aut@@ |
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2020-09-01T00:00:00Z |
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IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. 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| author |
Aliotta, Eric |
| spellingShingle |
Aliotta, Eric ddc 610 bkl 44.81 bkl 44.90 misc Lower grade glioma misc Diffusion weighted imaging misc Genetic classification misc IDH mutation Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
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Aliotta, Eric |
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electronic Article |
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610 - Medicine & health |
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springer |
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true |
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1573-7373 |
| topic_title |
610 ASE 44.81 bkl 44.90 bkl Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign Lower grade glioma (dpeaa)DE-He213 Diffusion weighted imaging (dpeaa)DE-He213 Genetic classification (dpeaa)DE-He213 IDH mutation (dpeaa)DE-He213 |
| topic |
ddc 610 bkl 44.81 bkl 44.90 misc Lower grade glioma misc Diffusion weighted imaging misc Genetic classification misc IDH mutation |
| topic_unstemmed |
ddc 610 bkl 44.81 bkl 44.90 misc Lower grade glioma misc Diffusion weighted imaging misc Genetic classification misc IDH mutation |
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ddc 610 bkl 44.81 bkl 44.90 misc Lower grade glioma misc Diffusion weighted imaging misc Genetic classification misc IDH mutation |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Journal of neuro-oncology |
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32046122X |
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610 - Medicine & health |
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Journal of neuro-oncology |
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(DE-627)32046122X (DE-600)2007293-4 |
| title |
Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
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(DE-627)SPR041237803 (SPR)s11060-020-03611-8-e |
| title_full |
Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
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Aliotta, Eric |
| journal |
Journal of neuro-oncology |
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Journal of neuro-oncology |
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eng |
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600 - Technology |
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2020 |
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325 |
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Aliotta, Eric Dutta, Sunil W. Feng, Xue Tustison, Nicholas J. Batchala, Prem P. Schiff, David Lopes, M. Beatriz Jain, Rajan Druzgal, T. Jason Mukherjee, Sugoto Patel, Sohil H. |
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149 |
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610 ASE 44.81 bkl 44.90 bkl |
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Elektronische Aufsätze |
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Aliotta, Eric |
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10.1007/s11060-020-03611-8 |
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610 |
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verfasserin |
| title_sort |
automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the t2-flair mismatch sign |
| title_auth |
Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
| abstract |
Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. |
| abstractGer |
Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. |
| abstract_unstemmed |
Purpose The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with $ V_{ADC>1.5} $ ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign—all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited $ V_{ADC>1.5} $ ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. Conclusion Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with $ V_{ADC>1.5} $ achieving > 90% classification specificity in both internal and validation cohorts. $ V_{ADC>1.5} $ exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG. |
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Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign |
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https://dx.doi.org/10.1007/s11060-020-03611-8 |
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Dutta, Sunil W. Feng, Xue Tustison, Nicholas J. Batchala, Prem P. Schiff, David Lopes, M. Beatriz Jain, Rajan Druzgal, T. Jason Mukherjee, Sugoto Patel, Sohil H. |
| author2Str |
Dutta, Sunil W. Feng, Xue Tustison, Nicholas J. Batchala, Prem P. Schiff, David Lopes, M. Beatriz Jain, Rajan Druzgal, T. Jason Mukherjee, Sugoto Patel, Sohil H. |
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10.1007/s11060-020-03611-8 |
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2024-07-03T20:59:05.473Z |
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IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. Methods Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × $ 10^{−3} %$ mm^{2} $/s ($ V_{ADC>1.5} $), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. Results $ V_{ADC>1.5} $ classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. 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| score |
7.399872 |