Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients
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 M...
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| Veröffentlicht in: | Journal of digital imaging 2024-08, Vol.37 (4), p.1336-1345 |
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| creator | Lin, Xueyao Wang, Chaochao Zheng, Jingjing Liu, Mengru Li, Ming Xu, Hongbin Dong, Haibo |
| description | 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. |
| doi_str_mv | 10.1007/s10278-024-00984-4 |
| format | Article |
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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.</description><identifier>ISSN: 2948-2933</identifier><identifier>ISSN: 0897-1889</identifier><identifier>ISSN: 2948-2925</identifier><identifier>EISSN: 2948-2933</identifier><identifier>EISSN: 1618-727X</identifier><identifier>DOI: 10.1007/s10278-024-00984-4</identifier><identifier>PMID: 38378963</identifier><language>eng</language><publisher>Switzerland: Springer Nature B.V</publisher><subject>Adolescent ; Adult ; Aged ; ATRX protein ; Brain ; Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - genetics ; Brain Neoplasms - pathology ; Brain tumors ; Diffusion Magnetic Resonance Imaging - methods ; Edema ; Female ; Genomics ; Glioma ; Glioma - diagnostic imaging ; Glioma - genetics ; Glioma - pathology ; Humans ; Intellectual disabilities ; Male ; Medical imaging ; Middle Aged ; Mutation ; Neuroimaging ; Nomograms ; Parameters ; Point mutation ; Retrospective Studies ; Solid tumors ; Thalassemia ; X-linked Nuclear Protein - genetics ; X-linked Nuclear Protein - metabolism ; Young Adult</subject><ispartof>Journal of digital imaging, 2024-08, Vol.37 (4), p.1336-1345</ispartof><rights>2024. 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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. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c356t-7619b02cbe3b486ecf250ed56e348757e0cdd97911f108b65ea44231aac78b2f3</cites><orcidid>0009-0009-4971-0031</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300756/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300756/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38378963$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Xueyao</creatorcontrib><creatorcontrib>Wang, Chaochao</creatorcontrib><creatorcontrib>Zheng, Jingjing</creatorcontrib><creatorcontrib>Liu, Mengru</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Xu, Hongbin</creatorcontrib><creatorcontrib>Dong, Haibo</creatorcontrib><title>Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients</title><title>Journal of digital imaging</title><addtitle>J Imaging Inform Med</addtitle><description>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.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>ATRX protein</subject><subject>Brain</subject><subject>Brain Neoplasms - diagnostic imaging</subject><subject>Brain Neoplasms - genetics</subject><subject>Brain Neoplasms - pathology</subject><subject>Brain tumors</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Edema</subject><subject>Female</subject><subject>Genomics</subject><subject>Glioma</subject><subject>Glioma - diagnostic imaging</subject><subject>Glioma - genetics</subject><subject>Glioma - pathology</subject><subject>Humans</subject><subject>Intellectual disabilities</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Neuroimaging</subject><subject>Nomograms</subject><subject>Parameters</subject><subject>Point mutation</subject><subject>Retrospective Studies</subject><subject>Solid tumors</subject><subject>Thalassemia</subject><subject>X-linked Nuclear Protein - genetics</subject><subject>X-linked Nuclear Protein - metabolism</subject><subject>Young Adult</subject><issn>2948-2933</issn><issn>0897-1889</issn><issn>2948-2925</issn><issn>2948-2933</issn><issn>1618-727X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdUdtu1DAQtRAVrbb9AR6QJV54SfElcewn1BZYVupNUC5vluNMsq4Su9hJBX_BJ-PttlVBsjSjmXPOeOYg9JKSQ0pI_TZRwmpZEFYWhChZFuUztMdUKQumOH_-JN9FByldE0I4p5wL8gLtcslrqQTfQ39Wo-kBX4zOJnwextBHM-Jjk6DFweOVt2ENEfyEz8LkcuW967o55az4Dq5fTxm3kXC-x87jb-EXDAlfRmidnRI-uvr8Ay_BAz6bJ3Mn8CXHOeHQ4eNoMmU5uDAafJm7eUzaRzudGRIc3McF-vrxw9XJp-L0Yrk6OTotLK_EVNSCqoYw2wBvSinAdqwi0FYCeCnrqgZi21bVitKOEtmICkxZMk6NsbVsWMcX6N1W92ZuRmhtnh3NoG-iG038rYNx-t-Od2vdh1tNKc_3r0RWeHOvEMPPGdKkR5csDIPxEOakmWKqypfOb4Fe_we9DnP0eT_NicxOSCnKjGJblI0hpQjd428o0RvT9dZ0nU3Xd6brDenV0z0eKQ8W87_Qh6j1</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Lin, Xueyao</creator><creator>Wang, Chaochao</creator><creator>Zheng, Jingjing</creator><creator>Liu, Mengru</creator><creator>Li, Ming</creator><creator>Xu, Hongbin</creator><creator>Dong, Haibo</creator><general>Springer Nature B.V</general><general>Springer International Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0009-0009-4971-0031</orcidid></search><sort><creationdate>20240801</creationdate><title>Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients</title><author>Lin, Xueyao ; Wang, Chaochao ; Zheng, Jingjing ; Liu, Mengru ; Li, Ming ; Xu, Hongbin ; Dong, Haibo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-7619b02cbe3b486ecf250ed56e348757e0cdd97911f108b65ea44231aac78b2f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>ATRX protein</topic><topic>Brain</topic><topic>Brain Neoplasms - diagnostic imaging</topic><topic>Brain Neoplasms - genetics</topic><topic>Brain Neoplasms - pathology</topic><topic>Brain tumors</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Edema</topic><topic>Female</topic><topic>Genomics</topic><topic>Glioma</topic><topic>Glioma - diagnostic imaging</topic><topic>Glioma - genetics</topic><topic>Glioma - pathology</topic><topic>Humans</topic><topic>Intellectual disabilities</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Middle Aged</topic><topic>Mutation</topic><topic>Neuroimaging</topic><topic>Nomograms</topic><topic>Parameters</topic><topic>Point mutation</topic><topic>Retrospective Studies</topic><topic>Solid tumors</topic><topic>Thalassemia</topic><topic>X-linked Nuclear Protein - genetics</topic><topic>X-linked Nuclear Protein - metabolism</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Xueyao</creatorcontrib><creatorcontrib>Wang, Chaochao</creatorcontrib><creatorcontrib>Zheng, Jingjing</creatorcontrib><creatorcontrib>Liu, Mengru</creatorcontrib><creatorcontrib>Li, Ming</creatorcontrib><creatorcontrib>Xu, Hongbin</creatorcontrib><creatorcontrib>Dong, Haibo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of digital imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Xueyao</au><au>Wang, Chaochao</au><au>Zheng, Jingjing</au><au>Liu, Mengru</au><au>Li, Ming</au><au>Xu, Hongbin</au><au>Dong, Haibo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients</atitle><jtitle>Journal of digital imaging</jtitle><addtitle>J Imaging Inform Med</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>37</volume><issue>4</issue><spage>1336</spage><epage>1345</epage><pages>1336-1345</pages><issn>2948-2933</issn><issn>0897-1889</issn><issn>2948-2925</issn><eissn>2948-2933</eissn><eissn>1618-727X</eissn><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.</abstract><cop>Switzerland</cop><pub>Springer Nature B.V</pub><pmid>38378963</pmid><doi>10.1007/s10278-024-00984-4</doi><tpages>10</tpages><orcidid>https://orcid.org/0009-0009-4971-0031</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adult Aged ATRX protein Brain Brain Neoplasms - diagnostic imaging Brain Neoplasms - genetics Brain Neoplasms - pathology Brain tumors Diffusion Magnetic Resonance Imaging - methods Edema Female Genomics Glioma Glioma - diagnostic imaging Glioma - genetics Glioma - pathology Humans Intellectual disabilities Male Medical imaging Middle Aged Mutation Neuroimaging Nomograms Parameters Point mutation Retrospective Studies Solid tumors Thalassemia X-linked Nuclear Protein - genetics X-linked Nuclear Protein - metabolism Young Adult |
| title | 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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