Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Maikel Verduin [verfasserIn] Sergey Primakov [verfasserIn] Inge Compter [verfasserIn] Henry C. Woodruff [verfasserIn] Sander M. J. van Kuijk [verfasserIn] Bram L. T. Ramaekers [verfasserIn] Maarten te Dorsthorst [verfasserIn] Elles G. M. Revenich [verfasserIn] Mark ter Laan [verfasserIn] Sjoert A. H. Pegge [verfasserIn] Frederick J. A. Meijer [verfasserIn] Jan Beckervordersandforth [verfasserIn] Ernst Jan Speel [verfasserIn] Benno Kusters [verfasserIn] Wendy W. J. de Leng [verfasserIn] Monique M. Anten [verfasserIn] Martijn P. G. Broen [verfasserIn] Linda Ackermans [verfasserIn] Olaf E. M. G. Schijns [verfasserIn] Onno Teernstra [verfasserIn] Koos Hovinga [verfasserIn] Marc A. Vooijs [verfasserIn] Vivianne C. G. Tjan-Heijnen [verfasserIn] Danielle B. P. Eekers [verfasserIn] Alida A. Postma [verfasserIn] Philippe Lambin [verfasserIn] Ann Hoeben [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	glioblastoma radiomics MRI prognosis prediction machine learning

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 13(2021), 4, p 722
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:4, p 722

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers13040722

Katalog-ID:	DOAJ00654486X

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520			\|a Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
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700	0		\|a Mark ter Laan \|e verfasserin \|4 aut
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700	0		\|a Jan Beckervordersandforth \|e verfasserin \|4 aut
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allfields	10.3390/cancers13040722 doi (DE-627)DOAJ00654486X (DE-599)DOAJf4f34d3d094745acb5e1e629272e2805 DE-627 ger DE-627 rakwb eng RC254-282 Maikel Verduin verfasserin aut Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sergey Primakov verfasserin aut Inge Compter verfasserin aut Henry C. Woodruff verfasserin aut Sander M. J. van Kuijk verfasserin aut Bram L. T. Ramaekers verfasserin aut Maarten te Dorsthorst verfasserin aut Elles G. M. Revenich verfasserin aut Mark ter Laan verfasserin aut Sjoert A. H. Pegge verfasserin aut Frederick J. A. Meijer verfasserin aut Jan Beckervordersandforth verfasserin aut Ernst Jan Speel verfasserin aut Benno Kusters verfasserin aut Wendy W. J. de Leng verfasserin aut Monique M. Anten verfasserin aut Martijn P. G. Broen verfasserin aut Linda Ackermans verfasserin aut Olaf E. M. G. Schijns verfasserin aut Onno Teernstra verfasserin aut Koos Hovinga verfasserin aut Marc A. Vooijs verfasserin aut Vivianne C. G. Tjan-Heijnen verfasserin aut Danielle B. P. Eekers verfasserin aut Alida A. Postma verfasserin aut Philippe Lambin verfasserin aut Ann Hoeben verfasserin aut In Cancers MDPI AG, 2010 13(2021), 4, p 722 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:4, p 722 https://doi.org/10.3390/cancers13040722 kostenfrei https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 kostenfrei https://www.mdpi.com/2072-6694/13/4/722 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 4, p 722
spelling	10.3390/cancers13040722 doi (DE-627)DOAJ00654486X (DE-599)DOAJf4f34d3d094745acb5e1e629272e2805 DE-627 ger DE-627 rakwb eng RC254-282 Maikel Verduin verfasserin aut Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sergey Primakov verfasserin aut Inge Compter verfasserin aut Henry C. Woodruff verfasserin aut Sander M. J. van Kuijk verfasserin aut Bram L. T. Ramaekers verfasserin aut Maarten te Dorsthorst verfasserin aut Elles G. M. Revenich verfasserin aut Mark ter Laan verfasserin aut Sjoert A. H. Pegge verfasserin aut Frederick J. A. Meijer verfasserin aut Jan Beckervordersandforth verfasserin aut Ernst Jan Speel verfasserin aut Benno Kusters verfasserin aut Wendy W. J. de Leng verfasserin aut Monique M. Anten verfasserin aut Martijn P. G. Broen verfasserin aut Linda Ackermans verfasserin aut Olaf E. M. G. Schijns verfasserin aut Onno Teernstra verfasserin aut Koos Hovinga verfasserin aut Marc A. Vooijs verfasserin aut Vivianne C. G. Tjan-Heijnen verfasserin aut Danielle B. P. Eekers verfasserin aut Alida A. Postma verfasserin aut Philippe Lambin verfasserin aut Ann Hoeben verfasserin aut In Cancers MDPI AG, 2010 13(2021), 4, p 722 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:4, p 722 https://doi.org/10.3390/cancers13040722 kostenfrei https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 kostenfrei https://www.mdpi.com/2072-6694/13/4/722 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 4, p 722
allfields_unstemmed	10.3390/cancers13040722 doi (DE-627)DOAJ00654486X (DE-599)DOAJf4f34d3d094745acb5e1e629272e2805 DE-627 ger DE-627 rakwb eng RC254-282 Maikel Verduin verfasserin aut Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sergey Primakov verfasserin aut Inge Compter verfasserin aut Henry C. Woodruff verfasserin aut Sander M. J. van Kuijk verfasserin aut Bram L. T. Ramaekers verfasserin aut Maarten te Dorsthorst verfasserin aut Elles G. M. Revenich verfasserin aut Mark ter Laan verfasserin aut Sjoert A. H. Pegge verfasserin aut Frederick J. A. Meijer verfasserin aut Jan Beckervordersandforth verfasserin aut Ernst Jan Speel verfasserin aut Benno Kusters verfasserin aut Wendy W. J. de Leng verfasserin aut Monique M. Anten verfasserin aut Martijn P. G. Broen verfasserin aut Linda Ackermans verfasserin aut Olaf E. M. G. Schijns verfasserin aut Onno Teernstra verfasserin aut Koos Hovinga verfasserin aut Marc A. Vooijs verfasserin aut Vivianne C. G. Tjan-Heijnen verfasserin aut Danielle B. P. Eekers verfasserin aut Alida A. Postma verfasserin aut Philippe Lambin verfasserin aut Ann Hoeben verfasserin aut In Cancers MDPI AG, 2010 13(2021), 4, p 722 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:4, p 722 https://doi.org/10.3390/cancers13040722 kostenfrei https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 kostenfrei https://www.mdpi.com/2072-6694/13/4/722 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 4, p 722
allfieldsGer	10.3390/cancers13040722 doi (DE-627)DOAJ00654486X (DE-599)DOAJf4f34d3d094745acb5e1e629272e2805 DE-627 ger DE-627 rakwb eng RC254-282 Maikel Verduin verfasserin aut Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sergey Primakov verfasserin aut Inge Compter verfasserin aut Henry C. Woodruff verfasserin aut Sander M. J. van Kuijk verfasserin aut Bram L. T. Ramaekers verfasserin aut Maarten te Dorsthorst verfasserin aut Elles G. M. Revenich verfasserin aut Mark ter Laan verfasserin aut Sjoert A. H. Pegge verfasserin aut Frederick J. A. Meijer verfasserin aut Jan Beckervordersandforth verfasserin aut Ernst Jan Speel verfasserin aut Benno Kusters verfasserin aut Wendy W. J. de Leng verfasserin aut Monique M. Anten verfasserin aut Martijn P. G. Broen verfasserin aut Linda Ackermans verfasserin aut Olaf E. M. G. Schijns verfasserin aut Onno Teernstra verfasserin aut Koos Hovinga verfasserin aut Marc A. Vooijs verfasserin aut Vivianne C. G. Tjan-Heijnen verfasserin aut Danielle B. P. Eekers verfasserin aut Alida A. Postma verfasserin aut Philippe Lambin verfasserin aut Ann Hoeben verfasserin aut In Cancers MDPI AG, 2010 13(2021), 4, p 722 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:4, p 722 https://doi.org/10.3390/cancers13040722 kostenfrei https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 kostenfrei https://www.mdpi.com/2072-6694/13/4/722 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 4, p 722
allfieldsSound	10.3390/cancers13040722 doi (DE-627)DOAJ00654486X (DE-599)DOAJf4f34d3d094745acb5e1e629272e2805 DE-627 ger DE-627 rakwb eng RC254-282 Maikel Verduin verfasserin aut Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted. glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Sergey Primakov verfasserin aut Inge Compter verfasserin aut Henry C. Woodruff verfasserin aut Sander M. J. van Kuijk verfasserin aut Bram L. T. Ramaekers verfasserin aut Maarten te Dorsthorst verfasserin aut Elles G. M. Revenich verfasserin aut Mark ter Laan verfasserin aut Sjoert A. H. Pegge verfasserin aut Frederick J. A. Meijer verfasserin aut Jan Beckervordersandforth verfasserin aut Ernst Jan Speel verfasserin aut Benno Kusters verfasserin aut Wendy W. J. de Leng verfasserin aut Monique M. Anten verfasserin aut Martijn P. G. Broen verfasserin aut Linda Ackermans verfasserin aut Olaf E. M. G. Schijns verfasserin aut Onno Teernstra verfasserin aut Koos Hovinga verfasserin aut Marc A. Vooijs verfasserin aut Vivianne C. G. Tjan-Heijnen verfasserin aut Danielle B. P. Eekers verfasserin aut Alida A. Postma verfasserin aut Philippe Lambin verfasserin aut Ann Hoeben verfasserin aut In Cancers MDPI AG, 2010 13(2021), 4, p 722 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:13 year:2021 number:4, p 722 https://doi.org/10.3390/cancers13040722 kostenfrei https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 kostenfrei https://www.mdpi.com/2072-6694/13/4/722 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 4, p 722
language	English
source	In Cancers 13(2021), 4, p 722 volume:13 year:2021 number:4, p 722
sourceStr	In Cancers 13(2021), 4, p 722 volume:13 year:2021 number:4, p 722
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	glioblastoma radiomics MRI prognosis prediction machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Cancers
authorswithroles_txt_mv	Maikel Verduin @@aut@@ Sergey Primakov @@aut@@ Inge Compter @@aut@@ Henry C. Woodruff @@aut@@ Sander M. J. van Kuijk @@aut@@ Bram L. T. Ramaekers @@aut@@ Maarten te Dorsthorst @@aut@@ Elles G. M. Revenich @@aut@@ Mark ter Laan @@aut@@ Sjoert A. H. Pegge @@aut@@ Frederick J. A. Meijer @@aut@@ Jan Beckervordersandforth @@aut@@ Ernst Jan Speel @@aut@@ Benno Kusters @@aut@@ Wendy W. J. de Leng @@aut@@ Monique M. Anten @@aut@@ Martijn P. G. Broen @@aut@@ Linda Ackermans @@aut@@ Olaf E. M. G. Schijns @@aut@@ Onno Teernstra @@aut@@ Koos Hovinga @@aut@@ Marc A. Vooijs @@aut@@ Vivianne C. G. Tjan-Heijnen @@aut@@ Danielle B. P. Eekers @@aut@@ Alida A. Postma @@aut@@ Philippe Lambin @@aut@@ Ann Hoeben @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	614095670
id	DOAJ00654486X
language_de	englisch
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Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. 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callnumber-first	R - Medicine
author	Maikel Verduin
spellingShingle	Maikel Verduin misc RC254-282 misc glioblastoma misc radiomics misc MRI misc prognosis misc prediction misc machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
authorStr	Maikel Verduin
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topic_title	RC254-282 Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma glioblastoma radiomics MRI prognosis prediction machine learning
topic	misc RC254-282 misc glioblastoma misc radiomics misc MRI misc prognosis misc prediction misc machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc glioblastoma misc radiomics misc MRI misc prognosis misc prediction misc machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc glioblastoma misc radiomics misc MRI misc prognosis misc prediction misc machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
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title_full	Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
author_sort	Maikel Verduin
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author_browse	Maikel Verduin Sergey Primakov Inge Compter Henry C. Woodruff Sander M. J. van Kuijk Bram L. T. Ramaekers Maarten te Dorsthorst Elles G. M. Revenich Mark ter Laan Sjoert A. H. Pegge Frederick J. A. Meijer Jan Beckervordersandforth Ernst Jan Speel Benno Kusters Wendy W. J. de Leng Monique M. Anten Martijn P. G. Broen Linda Ackermans Olaf E. M. G. Schijns Onno Teernstra Koos Hovinga Marc A. Vooijs Vivianne C. G. Tjan-Heijnen Danielle B. P. Eekers Alida A. Postma Philippe Lambin Ann Hoeben
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doi_str_mv	10.3390/cancers13040722
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title_sort	prognostic and predictive value of integrated qualitative and quantitative magnetic resonance imaging analysis in glioblastoma
callnumber	RC254-282
title_auth	Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
abstract	Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
abstractGer	Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
abstract_unstemmed	Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (<i<p</i<-value < 0.001). The predictive models performed significantly in the external validation for <i<EGFR</i< amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and <i<MGMT</i<-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for <i<IDH</i<-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
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title_short	Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma
url	https://doi.org/10.3390/cancers13040722 https://doaj.org/article/f4f34d3d094745acb5e1e629272e2805 https://www.mdpi.com/2072-6694/13/4/722 https://doaj.org/toc/2072-6694
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up_date	2024-07-03T21:33:32.982Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ00654486X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414080136.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/cancers13040722</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ00654486X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJf4f34d3d094745acb5e1e629272e2805</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Maikel Verduin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (<i<IDH</i<)-wild type GBM. Predictive models for <i<IDH</i<-mutation, 06-methylguanine-DNA-methyltransferase (<i<MGMT</i<)-methylation and epidermal growth factor receptor (<i<EGFR</i<) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. 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Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

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