Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients
Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Xueyao [verfasserIn] Wang, Chaochao [verfasserIn] Zheng, Jingjing [verfasserIn] Liu, Mengru [verfasserIn] Li, Ming [verfasserIn] Xu, Hongbin [verfasserIn] Dong, Haibo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of imaging informatics in medicine - Springer International Publishing, 2024, 37(2024), 4 vom: 20. Feb., Seite 1336-1345 |
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| Übergeordnetes Werk: |
volume:37 ; year:2024 ; number:4 ; day:20 ; month:02 ; pages:1336-1345 |
| Links: |
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| DOI / URN: |
10.1007/s10278-024-00984-4 |
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SPR056850999 |
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| 520 | |a Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. | ||
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| 700 | 1 | |a Xu, Hongbin |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Haibo |e verfasserin |4 aut | |
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10.1007/s10278-024-00984-4 doi (DE-627)SPR056850999 (SPR)s10278-024-00984-4-e DE-627 ger DE-627 rakwb eng 610 VZ Lin, Xueyao verfasserin (orcid)0009-0009-4971-0031 aut Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Wang, Chaochao verfasserin aut Zheng, Jingjing verfasserin aut Liu, Mengru verfasserin aut Li, Ming verfasserin aut Xu, Hongbin verfasserin aut Dong, Haibo verfasserin aut Enthalten in Journal of imaging informatics in medicine Springer International Publishing, 2024 37(2024), 4 vom: 20. Feb., Seite 1336-1345 Online-Ressource (DE-627)188290527X (DE-600)3181204-1 2948-2933 nnns volume:37 year:2024 number:4 day:20 month:02 pages:1336-1345 https://dx.doi.org/10.1007/s10278-024-00984-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_138 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 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 37 2024 4 20 02 1336-1345 |
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10.1007/s10278-024-00984-4 doi (DE-627)SPR056850999 (SPR)s10278-024-00984-4-e DE-627 ger DE-627 rakwb eng 610 VZ Lin, Xueyao verfasserin (orcid)0009-0009-4971-0031 aut Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Wang, Chaochao verfasserin aut Zheng, Jingjing verfasserin aut Liu, Mengru verfasserin aut Li, Ming verfasserin aut Xu, Hongbin verfasserin aut Dong, Haibo verfasserin aut Enthalten in Journal of imaging informatics in medicine Springer International Publishing, 2024 37(2024), 4 vom: 20. Feb., Seite 1336-1345 Online-Ressource (DE-627)188290527X (DE-600)3181204-1 2948-2933 nnns volume:37 year:2024 number:4 day:20 month:02 pages:1336-1345 https://dx.doi.org/10.1007/s10278-024-00984-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_138 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 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 37 2024 4 20 02 1336-1345 |
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10.1007/s10278-024-00984-4 doi (DE-627)SPR056850999 (SPR)s10278-024-00984-4-e DE-627 ger DE-627 rakwb eng 610 VZ Lin, Xueyao verfasserin (orcid)0009-0009-4971-0031 aut Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Wang, Chaochao verfasserin aut Zheng, Jingjing verfasserin aut Liu, Mengru verfasserin aut Li, Ming verfasserin aut Xu, Hongbin verfasserin aut Dong, Haibo verfasserin aut Enthalten in Journal of imaging informatics in medicine Springer International Publishing, 2024 37(2024), 4 vom: 20. Feb., Seite 1336-1345 Online-Ressource (DE-627)188290527X (DE-600)3181204-1 2948-2933 nnns volume:37 year:2024 number:4 day:20 month:02 pages:1336-1345 https://dx.doi.org/10.1007/s10278-024-00984-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_138 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 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 37 2024 4 20 02 1336-1345 |
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10.1007/s10278-024-00984-4 doi (DE-627)SPR056850999 (SPR)s10278-024-00984-4-e DE-627 ger DE-627 rakwb eng 610 VZ Lin, Xueyao verfasserin (orcid)0009-0009-4971-0031 aut Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Wang, Chaochao verfasserin aut Zheng, Jingjing verfasserin aut Liu, Mengru verfasserin aut Li, Ming verfasserin aut Xu, Hongbin verfasserin aut Dong, Haibo verfasserin aut Enthalten in Journal of imaging informatics in medicine Springer International Publishing, 2024 37(2024), 4 vom: 20. Feb., Seite 1336-1345 Online-Ressource (DE-627)188290527X (DE-600)3181204-1 2948-2933 nnns volume:37 year:2024 number:4 day:20 month:02 pages:1336-1345 https://dx.doi.org/10.1007/s10278-024-00984-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_138 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 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 37 2024 4 20 02 1336-1345 |
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10.1007/s10278-024-00984-4 doi (DE-627)SPR056850999 (SPR)s10278-024-00984-4-e DE-627 ger DE-627 rakwb eng 610 VZ Lin, Xueyao verfasserin (orcid)0009-0009-4971-0031 aut Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Wang, Chaochao verfasserin aut Zheng, Jingjing verfasserin aut Liu, Mengru verfasserin aut Li, Ming verfasserin aut Xu, Hongbin verfasserin aut Dong, Haibo verfasserin aut Enthalten in Journal of imaging informatics in medicine Springer International Publishing, 2024 37(2024), 4 vom: 20. Feb., Seite 1336-1345 Online-Ressource (DE-627)188290527X (DE-600)3181204-1 2948-2933 nnns volume:37 year:2024 number:4 day:20 month:02 pages:1336-1345 https://dx.doi.org/10.1007/s10278-024-00984-4 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_138 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_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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 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 37 2024 4 20 02 1336-1345 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). 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Lin, Xueyao |
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Lin, Xueyao ddc 610 misc Glioma misc IVIM-DWI misc Radiomics misc Diagnosis Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients |
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610 VZ Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients Glioma (dpeaa)DE-He213 IVIM-DWI (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 |
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Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients |
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Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients |
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Lin, Xueyao Wang, Chaochao Zheng, Jingjing Liu, Mengru Li, Ming Xu, Hongbin Dong, Haibo |
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image omics nomogram based on incoherent motion diffusion-weighted imaging in voxels predicts atrx gene mutation status of brain glioma patients |
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Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients |
| abstract |
Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This study aimed to construct an imaging genomics nomogram based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha thalassemia/mental retardation syndrome X-linked (ATRX) gene in patients with brain gliomas. We retrospectively analyzed routine MR and IVIM-DWI data from 85 patients with pathologically confirmed brain gliomas from January 2017 to May 2023. The data were divided into a training set (N=61) and a test set (N=24) in a 7:3 ratio. Regions of interest (ROIs) of brain gliomas, including the solid tumor region (rCET), edema region (rE), and necrotic region (rNec), were delineated using 3D-Slicer software and projected onto the D, D*, and f sequences. A total of 1037 features were extracted from each ROI, resulting in 3111 features per patient. Age was incorporated in the calculation of the Radscore, and a clinical-imaging genomics combined model was constructed, from which a nomogram graph was generated. Separate models were built for the D, D*, and f parameters. The AUC value of the D parameter model was 0.97 (95% CI: 0.93–1.00) in the training set and 0.91 (95% CI: 0.79–1.00) in the validation set, which was significantly higher than that of the D* parameter model (0.90, 0.82) and the f parameter model (0.89, 0.91). The imaging genomics nomogram based on IVIM-DWI can effectively predict the ATRX gene status of patients with brain gliomas, with the D parameter showing the highest efficacy. © The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients |
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https://dx.doi.org/10.1007/s10278-024-00984-4 |
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| author2 |
Wang, Chaochao Zheng, Jingjing Liu, Mengru Li, Ming Xu, Hongbin Dong, Haibo |
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Wang, Chaochao Zheng, Jingjing Liu, Mengru Li, Ming Xu, Hongbin Dong, Haibo |
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10.1007/s10278-024-00984-4 |
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2024-08-06T04:49:57.758Z |
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| score |
7.401099 |