A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiform...
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| Veröffentlicht in: | Scientific reports 2017-09, Vol.7 (1), p.10353-8, Article 10353 |
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| description | Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P |
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This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-017-10649-8</identifier><identifier>PMID: 28871110</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>59/57 ; 631/114/1305 ; 631/114/1564 ; 639/166/985 ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Archives & records ; Brain cancer ; Cancer ; Child ; Datasets ; Deep Learning ; Edema ; Feature selection ; Female ; Geometry ; Glioblastoma ; Glioblastoma - diagnostic imaging ; Glioblastoma - mortality ; Humanities and Social Sciences ; Humans ; Image Interpretation, Computer-Assisted ; Image Processing, Computer-Assisted ; Kaplan-Meier Estimate ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Medical prognosis ; Middle Aged ; Models, Theoretical ; multidisciplinary ; Neural networks ; Nomograms ; Oncology ; Open source software ; Patients ; Prognosis ; Public domain ; Radiomics ; Reproducibility of Results ; Risk factors ; ROC Curve ; Science ; Science (multidisciplinary) ; Survival ; Transfer learning ; Young Adult</subject><ispartof>Scientific reports, 2017-09, Vol.7 (1), p.10353-8, Article 10353</ispartof><rights>The Author(s) 2017</rights><rights>2017. 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Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-6396fcdf614f9041e375239c8fac6972fade895ecf25e76e467252f7cdcef4c43</citedby><cites>FETCH-LOGICAL-c540t-6396fcdf614f9041e375239c8fac6972fade895ecf25e76e467252f7cdcef4c43</cites><orcidid>0000-0001-8165-9322 ; 0000-0003-4140-0580</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/PMC5583361/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583361/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28871110$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lao, Jiangwei</creatorcontrib><creatorcontrib>Chen, Yinsheng</creatorcontrib><creatorcontrib>Li, Zhi-Cheng</creatorcontrib><creatorcontrib>Li, Qihua</creatorcontrib><creatorcontrib>Zhang, Ji</creatorcontrib><creatorcontrib>Liu, Jing</creatorcontrib><creatorcontrib>Zhai, Guangtao</creatorcontrib><title>A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.</description><subject>59/57</subject><subject>631/114/1305</subject><subject>631/114/1564</subject><subject>639/166/985</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Archives & records</subject><subject>Brain cancer</subject><subject>Cancer</subject><subject>Child</subject><subject>Datasets</subject><subject>Deep Learning</subject><subject>Edema</subject><subject>Feature selection</subject><subject>Female</subject><subject>Geometry</subject><subject>Glioblastoma</subject><subject>Glioblastoma - diagnostic imaging</subject><subject>Glioblastoma - mortality</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted</subject><subject>Image Processing, Computer-Assisted</subject><subject>Kaplan-Meier Estimate</subject><subject>Magnetic Resonance Imaging - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lao, Jiangwei</au><au>Chen, Yinsheng</au><au>Li, Zhi-Cheng</au><au>Li, Qihua</au><au>Zhang, Ji</au><au>Liu, Jing</au><au>Zhai, Guangtao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2017-09-04</date><risdate>2017</risdate><volume>7</volume><issue>1</issue><spage>10353</spage><epage>8</epage><pages>10353-8</pages><artnum>10353</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>28871110</pmid><doi>10.1038/s41598-017-10649-8</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-8165-9322</orcidid><orcidid>https://orcid.org/0000-0003-4140-0580</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 59/57 631/114/1305 631/114/1564 639/166/985 Adolescent Adult Aged Aged, 80 and over Algorithms Archives & records Brain cancer Cancer Child Datasets Deep Learning Edema Feature selection Female Geometry Glioblastoma Glioblastoma - diagnostic imaging Glioblastoma - mortality Humanities and Social Sciences Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Kaplan-Meier Estimate Magnetic Resonance Imaging - methods Male Medical imaging Medical prognosis Middle Aged Models, Theoretical multidisciplinary Neural networks Nomograms Oncology Open source software Patients Prognosis Public domain Radiomics Reproducibility of Results Risk factors ROC Curve Science Science (multidisciplinary) Survival Transfer learning Young Adult |
| title | A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme |
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