Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics
Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acqu...
Ausführliche Beschreibung
Autor*in: |
Mulford, Kellen L. [verfasserIn] |
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E-Artikel |
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Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Neuroradiology - Berlin : Springer, 1970, 64(2022), 3 vom: 19. Jan., Seite 603-609 |
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Übergeordnetes Werk: |
volume:64 ; year:2022 ; number:3 ; day:19 ; month:01 ; pages:603-609 |
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DOI / URN: |
10.1007/s00234-022-02900-5 |
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SPR046248978 |
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520 | |a Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. | ||
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10.1007/s00234-022-02900-5 doi (DE-627)SPR046248978 (SPR)s00234-022-02900-5-e DE-627 ger DE-627 rakwb eng Mulford, Kellen L. verfasserin aut Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Moen, Sean L. aut Grande, Andrew W. aut Nixdorf, Donald R. aut Van de Moortele, Pierre-Francois aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 3 vom: 19. Jan., Seite 603-609 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:3 day:19 month:01 pages:603-609 https://dx.doi.org/10.1007/s00234-022-02900-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 AR 64 2022 3 19 01 603-609 |
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10.1007/s00234-022-02900-5 doi (DE-627)SPR046248978 (SPR)s00234-022-02900-5-e DE-627 ger DE-627 rakwb eng Mulford, Kellen L. verfasserin aut Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Moen, Sean L. aut Grande, Andrew W. aut Nixdorf, Donald R. aut Van de Moortele, Pierre-Francois aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 3 vom: 19. Jan., Seite 603-609 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:3 day:19 month:01 pages:603-609 https://dx.doi.org/10.1007/s00234-022-02900-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 AR 64 2022 3 19 01 603-609 |
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10.1007/s00234-022-02900-5 doi (DE-627)SPR046248978 (SPR)s00234-022-02900-5-e DE-627 ger DE-627 rakwb eng Mulford, Kellen L. verfasserin aut Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Moen, Sean L. aut Grande, Andrew W. aut Nixdorf, Donald R. aut Van de Moortele, Pierre-Francois aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 3 vom: 19. Jan., Seite 603-609 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:3 day:19 month:01 pages:603-609 https://dx.doi.org/10.1007/s00234-022-02900-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 AR 64 2022 3 19 01 603-609 |
allfieldsGer |
10.1007/s00234-022-02900-5 doi (DE-627)SPR046248978 (SPR)s00234-022-02900-5-e DE-627 ger DE-627 rakwb eng Mulford, Kellen L. verfasserin aut Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Moen, Sean L. aut Grande, Andrew W. aut Nixdorf, Donald R. aut Van de Moortele, Pierre-Francois aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 3 vom: 19. Jan., Seite 603-609 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:3 day:19 month:01 pages:603-609 https://dx.doi.org/10.1007/s00234-022-02900-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 AR 64 2022 3 19 01 603-609 |
allfieldsSound |
10.1007/s00234-022-02900-5 doi (DE-627)SPR046248978 (SPR)s00234-022-02900-5-e DE-627 ger DE-627 rakwb eng Mulford, Kellen L. verfasserin aut Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Moen, Sean L. aut Grande, Andrew W. aut Nixdorf, Donald R. aut Van de Moortele, Pierre-Francois aut Enthalten in Neuroradiology Berlin : Springer, 1970 64(2022), 3 vom: 19. Jan., Seite 603-609 (DE-627)254638430 (DE-600)1462953-7 1432-1920 nnns volume:64 year:2022 number:3 day:19 month:01 pages:603-609 https://dx.doi.org/10.1007/s00234-022-02900-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 AR 64 2022 3 19 01 603-609 |
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This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. 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Mulford, Kellen L. |
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Mulford, Kellen L. misc MRI misc Trigeminal neuralgia misc Radiomics misc Machine learning Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics |
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Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics MRI (dpeaa)DE-He213 Trigeminal neuralgia (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 |
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Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics |
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identifying symptomatic trigeminal nerves from mri in a cohort of trigeminal neuralgia patients using radiomics |
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Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics |
abstract |
Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstractGer |
Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstract_unstemmed |
Introduction Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves. Methods 3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability. Results A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively. Conclusion Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics |
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https://dx.doi.org/10.1007/s00234-022-02900-5 |
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Moen, Sean L. Grande, Andrew W. Nixdorf, Donald R. Van de Moortele, Pierre-Francois |
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Moen, Sean L. Grande, Andrew W. Nixdorf, Donald R. Van de Moortele, Pierre-Francois |
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10.1007/s00234-022-02900-5 |
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2024-07-03T21:20:18.191Z |
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|
score |
7.4007874 |