Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery
Purpose Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predi...
Gespeichert in:
| Veröffentlicht in: | Journal of neuro-oncology 2020-02, Vol.146 (3), p.439-449 |
|---|---|
| Hauptverfasser: | , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 449 |
|---|---|
| container_issue | 3 |
| container_start_page | 439 |
| container_title | Journal of neuro-oncology |
| container_volume | 146 |
| creator | Huang, Chih-Ying Lee, Cheng-Chia Yang, Huai-Che Lin, Chung-Jung Wu, Hsiu-Mei Chung, Wen-Yuh Shiau, Cheng-Ying Guo, Wan-Yuo Pan, David Hung-Chi Peng, Syu-Jyun |
| description | Purpose
Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.
Methods
We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.
Results
Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712;
P
= .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699;
P
= .014) was independently associated with superior local tumor control.
Conclusions
The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS. |
| doi_str_mv | 10.1007/s11060-019-03343-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2351482303</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2351482303</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-14a5d408c958d9d833f7e684566fda37f9910396b66a0dd46742c113b1f9e1923</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouq7-AQ8S8OKlOtNJk-YooqsoCKLgLWSbVCvbVpMW8d-bdf0AD0KYBOaZN8PD2B7CEQKo44gIEjJAnQGRoEyssQkWijJFitbZBFCqrNDiYYttx_gMAEIRbrItyiFPbzFhD7fWNX3bVJHbyF9C_9j1cWgqXttq6ANvOj4PNtXWDzam4yMfgreDd_ytGZ74zLat5VddU3selllxDI8-vO-wjdouot_9uqfs_vzs7vQiu76ZXZ6eXGcVqWLIUNjCCSgrXZROu5KoVl6WopCydpZUrTUCaTmX0oJzQiqRV4g0x1p71DlN2eEqN-3-Ovo4mLaJlV8sbOf7MZqcChRlTsnQlB38QZ_7MXRpuyUFSmEhllS-oqrQxxh8bV5C09rwbhDM0rtZeTfJu_n0bkQa2v-KHuetdz8j36ITQCsgplaXBP3-_U_sB_WtjJE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2350771543</pqid></control><display><type>article</type><title>Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery</title><source>SpringerLink Journals - AutoHoldings</source><creator>Huang, Chih-Ying ; Lee, Cheng-Chia ; Yang, Huai-Che ; Lin, Chung-Jung ; Wu, Hsiu-Mei ; Chung, Wen-Yuh ; Shiau, Cheng-Ying ; Guo, Wan-Yuo ; Pan, David Hung-Chi ; Peng, Syu-Jyun</creator><creatorcontrib>Huang, Chih-Ying ; Lee, Cheng-Chia ; Yang, Huai-Che ; Lin, Chung-Jung ; Wu, Hsiu-Mei ; Chung, Wen-Yuh ; Shiau, Cheng-Ying ; Guo, Wan-Yuo ; Pan, David Hung-Chi ; Peng, Syu-Jyun</creatorcontrib><description>Purpose
Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.
Methods
We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.
Results
Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712;
P
= .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699;
P
= .014) was independently associated with superior local tumor control.
Conclusions
The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.</description><identifier>ISSN: 0167-594X</identifier><identifier>EISSN: 1573-7373</identifier><identifier>DOI: 10.1007/s11060-019-03343-4</identifier><identifier>PMID: 32020474</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Brain cancer ; Clinical Study ; Effectiveness ; Lung cancer ; Magnetic resonance imaging ; Medicine ; Medicine & Public Health ; Metastases ; Metastasis ; Neuroimaging ; Neurology ; Non-small cell lung carcinoma ; Oncology ; Patients ; Platinum ; Radiomics ; Radiosurgery ; Small cell lung carcinoma ; Toxicity ; Tumors</subject><ispartof>Journal of neuro-oncology, 2020-02, Vol.146 (3), p.439-449</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Journal of Neuro-Oncology is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-14a5d408c958d9d833f7e684566fda37f9910396b66a0dd46742c113b1f9e1923</citedby><cites>FETCH-LOGICAL-c375t-14a5d408c958d9d833f7e684566fda37f9910396b66a0dd46742c113b1f9e1923</cites><orcidid>0000-0001-5002-6581</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11060-019-03343-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11060-019-03343-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32020474$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Chih-Ying</creatorcontrib><creatorcontrib>Lee, Cheng-Chia</creatorcontrib><creatorcontrib>Yang, Huai-Che</creatorcontrib><creatorcontrib>Lin, Chung-Jung</creatorcontrib><creatorcontrib>Wu, Hsiu-Mei</creatorcontrib><creatorcontrib>Chung, Wen-Yuh</creatorcontrib><creatorcontrib>Shiau, Cheng-Ying</creatorcontrib><creatorcontrib>Guo, Wan-Yuo</creatorcontrib><creatorcontrib>Pan, David Hung-Chi</creatorcontrib><creatorcontrib>Peng, Syu-Jyun</creatorcontrib><title>Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery</title><title>Journal of neuro-oncology</title><addtitle>J Neurooncol</addtitle><addtitle>J Neurooncol</addtitle><description>Purpose
Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.
Methods
We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.
Results
Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712;
P
= .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699;
P
= .014) was independently associated with superior local tumor control.
Conclusions
The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.</description><subject>Brain cancer</subject><subject>Clinical Study</subject><subject>Effectiveness</subject><subject>Lung cancer</subject><subject>Magnetic resonance imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Neuroimaging</subject><subject>Neurology</subject><subject>Non-small cell lung carcinoma</subject><subject>Oncology</subject><subject>Patients</subject><subject>Platinum</subject><subject>Radiomics</subject><subject>Radiosurgery</subject><subject>Small cell lung carcinoma</subject><subject>Toxicity</subject><subject>Tumors</subject><issn>0167-594X</issn><issn>1573-7373</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LxDAQhoMouq7-AQ8S8OKlOtNJk-YooqsoCKLgLWSbVCvbVpMW8d-bdf0AD0KYBOaZN8PD2B7CEQKo44gIEjJAnQGRoEyssQkWijJFitbZBFCqrNDiYYttx_gMAEIRbrItyiFPbzFhD7fWNX3bVJHbyF9C_9j1cWgqXttq6ANvOj4PNtXWDzam4yMfgreDd_ytGZ74zLat5VddU3selllxDI8-vO-wjdouot_9uqfs_vzs7vQiu76ZXZ6eXGcVqWLIUNjCCSgrXZROu5KoVl6WopCydpZUrTUCaTmX0oJzQiqRV4g0x1p71DlN2eEqN-3-Ovo4mLaJlV8sbOf7MZqcChRlTsnQlB38QZ_7MXRpuyUFSmEhllS-oqrQxxh8bV5C09rwbhDM0rtZeTfJu_n0bkQa2v-KHuetdz8j36ITQCsgplaXBP3-_U_sB_WtjJE</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Huang, Chih-Ying</creator><creator>Lee, Cheng-Chia</creator><creator>Yang, Huai-Che</creator><creator>Lin, Chung-Jung</creator><creator>Wu, Hsiu-Mei</creator><creator>Chung, Wen-Yuh</creator><creator>Shiau, Cheng-Ying</creator><creator>Guo, Wan-Yuo</creator><creator>Pan, David Hung-Chi</creator><creator>Peng, Syu-Jyun</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5002-6581</orcidid></search><sort><creationdate>20200201</creationdate><title>Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery</title><author>Huang, Chih-Ying ; Lee, Cheng-Chia ; Yang, Huai-Che ; Lin, Chung-Jung ; Wu, Hsiu-Mei ; Chung, Wen-Yuh ; Shiau, Cheng-Ying ; Guo, Wan-Yuo ; Pan, David Hung-Chi ; Peng, Syu-Jyun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-14a5d408c958d9d833f7e684566fda37f9910396b66a0dd46742c113b1f9e1923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Brain cancer</topic><topic>Clinical Study</topic><topic>Effectiveness</topic><topic>Lung cancer</topic><topic>Magnetic resonance imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Neuroimaging</topic><topic>Neurology</topic><topic>Non-small cell lung carcinoma</topic><topic>Oncology</topic><topic>Patients</topic><topic>Platinum</topic><topic>Radiomics</topic><topic>Radiosurgery</topic><topic>Small cell lung carcinoma</topic><topic>Toxicity</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Chih-Ying</creatorcontrib><creatorcontrib>Lee, Cheng-Chia</creatorcontrib><creatorcontrib>Yang, Huai-Che</creatorcontrib><creatorcontrib>Lin, Chung-Jung</creatorcontrib><creatorcontrib>Wu, Hsiu-Mei</creatorcontrib><creatorcontrib>Chung, Wen-Yuh</creatorcontrib><creatorcontrib>Shiau, Cheng-Ying</creatorcontrib><creatorcontrib>Guo, Wan-Yuo</creatorcontrib><creatorcontrib>Pan, David Hung-Chi</creatorcontrib><creatorcontrib>Peng, Syu-Jyun</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of neuro-oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Chih-Ying</au><au>Lee, Cheng-Chia</au><au>Yang, Huai-Che</au><au>Lin, Chung-Jung</au><au>Wu, Hsiu-Mei</au><au>Chung, Wen-Yuh</au><au>Shiau, Cheng-Ying</au><au>Guo, Wan-Yuo</au><au>Pan, David Hung-Chi</au><au>Peng, Syu-Jyun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery</atitle><jtitle>Journal of neuro-oncology</jtitle><stitle>J Neurooncol</stitle><addtitle>J Neurooncol</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>146</volume><issue>3</issue><spage>439</spage><epage>449</epage><pages>439-449</pages><issn>0167-594X</issn><eissn>1573-7373</eissn><abstract>Purpose
Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.
Methods
We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.
Results
Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712;
P
= .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699;
P
= .014) was independently associated with superior local tumor control.
Conclusions
The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>32020474</pmid><doi>10.1007/s11060-019-03343-4</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-5002-6581</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0167-594X |
| ispartof | Journal of neuro-oncology, 2020-02, Vol.146 (3), p.439-449 |
| issn | 0167-594X 1573-7373 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2351482303 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Brain cancer Clinical Study Effectiveness Lung cancer Magnetic resonance imaging Medicine Medicine & Public Health Metastases Metastasis Neuroimaging Neurology Non-small cell lung carcinoma Oncology Patients Platinum Radiomics Radiosurgery Small cell lung carcinoma Toxicity Tumors |
| title | Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T17%3A39%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Radiomics%20as%20prognostic%20factor%20in%20brain%20metastases%20treated%20with%20Gamma%20Knife%20radiosurgery&rft.jtitle=Journal%20of%20neuro-oncology&rft.au=Huang,%20Chih-Ying&rft.date=2020-02-01&rft.volume=146&rft.issue=3&rft.spage=439&rft.epage=449&rft.pages=439-449&rft.issn=0167-594X&rft.eissn=1573-7373&rft_id=info:doi/10.1007/s11060-019-03343-4&rft_dat=%3Cproquest_cross%3E2351482303%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2350771543&rft_id=info:pmid/32020474&rfr_iscdi=true |