18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wiltgen, Tun [verfasserIn] Fleischmann, Daniel F. Kaiser, Lena Holzgreve, Adrien Corradini, Stefanie Landry, Guillaume Ingrisch, Michael Popp, Ilinca Grosu, Anca L. Unterrainer, Marcus Bartenstein, Peter Parodi, Katia Belka, Claus Albert, Nathalie Niyazi, Maximilian Riboldi, Marco

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Quantitative image analysis Radiomics Survival analysis Glioblastoma Radiotherapy

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Radiation oncology - London : BioMed Central, 2006, 17(2022), 1 vom: 02. Dez.
Übergeordnetes Werk:	volume:17 ; year:2022 ; number:1 ; day:02 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s13014-022-02164-6

Katalog-ID:	SPR051197820

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520			\|a Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.
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allfields	10.1186/s13014-022-02164-6 doi (DE-627)SPR051197820 (SPR)s13014-022-02164-6-e DE-627 ger DE-627 rakwb eng Wiltgen, Tun verfasserin aut 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213 Fleischmann, Daniel F. aut Kaiser, Lena aut Holzgreve, Adrien aut Corradini, Stefanie aut Landry, Guillaume aut Ingrisch, Michael aut Popp, Ilinca aut Grosu, Anca L. aut Unterrainer, Marcus aut Bartenstein, Peter aut Parodi, Katia aut Belka, Claus aut Albert, Nathalie aut Niyazi, Maximilian aut Riboldi, Marco aut Enthalten in Radiation oncology London : BioMed Central, 2006 17(2022), 1 vom: 02. Dez. (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:17 year:2022 number:1 day:02 month:12 https://dx.doi.org/10.1186/s13014-022-02164-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 17 2022 1 02 12
spelling	10.1186/s13014-022-02164-6 doi (DE-627)SPR051197820 (SPR)s13014-022-02164-6-e DE-627 ger DE-627 rakwb eng Wiltgen, Tun verfasserin aut 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213 Fleischmann, Daniel F. aut Kaiser, Lena aut Holzgreve, Adrien aut Corradini, Stefanie aut Landry, Guillaume aut Ingrisch, Michael aut Popp, Ilinca aut Grosu, Anca L. aut Unterrainer, Marcus aut Bartenstein, Peter aut Parodi, Katia aut Belka, Claus aut Albert, Nathalie aut Niyazi, Maximilian aut Riboldi, Marco aut Enthalten in Radiation oncology London : BioMed Central, 2006 17(2022), 1 vom: 02. Dez. (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:17 year:2022 number:1 day:02 month:12 https://dx.doi.org/10.1186/s13014-022-02164-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 17 2022 1 02 12
allfields_unstemmed	10.1186/s13014-022-02164-6 doi (DE-627)SPR051197820 (SPR)s13014-022-02164-6-e DE-627 ger DE-627 rakwb eng Wiltgen, Tun verfasserin aut 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213 Fleischmann, Daniel F. aut Kaiser, Lena aut Holzgreve, Adrien aut Corradini, Stefanie aut Landry, Guillaume aut Ingrisch, Michael aut Popp, Ilinca aut Grosu, Anca L. aut Unterrainer, Marcus aut Bartenstein, Peter aut Parodi, Katia aut Belka, Claus aut Albert, Nathalie aut Niyazi, Maximilian aut Riboldi, Marco aut Enthalten in Radiation oncology London : BioMed Central, 2006 17(2022), 1 vom: 02. Dez. (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:17 year:2022 number:1 day:02 month:12 https://dx.doi.org/10.1186/s13014-022-02164-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 17 2022 1 02 12
allfieldsGer	10.1186/s13014-022-02164-6 doi (DE-627)SPR051197820 (SPR)s13014-022-02164-6-e DE-627 ger DE-627 rakwb eng Wiltgen, Tun verfasserin aut 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213 Fleischmann, Daniel F. aut Kaiser, Lena aut Holzgreve, Adrien aut Corradini, Stefanie aut Landry, Guillaume aut Ingrisch, Michael aut Popp, Ilinca aut Grosu, Anca L. aut Unterrainer, Marcus aut Bartenstein, Peter aut Parodi, Katia aut Belka, Claus aut Albert, Nathalie aut Niyazi, Maximilian aut Riboldi, Marco aut Enthalten in Radiation oncology London : BioMed Central, 2006 17(2022), 1 vom: 02. Dez. (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:17 year:2022 number:1 day:02 month:12 https://dx.doi.org/10.1186/s13014-022-02164-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 17 2022 1 02 12
allfieldsSound	10.1186/s13014-022-02164-6 doi (DE-627)SPR051197820 (SPR)s13014-022-02164-6-e DE-627 ger DE-627 rakwb eng Wiltgen, Tun verfasserin aut 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213 Fleischmann, Daniel F. aut Kaiser, Lena aut Holzgreve, Adrien aut Corradini, Stefanie aut Landry, Guillaume aut Ingrisch, Michael aut Popp, Ilinca aut Grosu, Anca L. aut Unterrainer, Marcus aut Bartenstein, Peter aut Parodi, Katia aut Belka, Claus aut Albert, Nathalie aut Niyazi, Maximilian aut Riboldi, Marco aut Enthalten in Radiation oncology London : BioMed Central, 2006 17(2022), 1 vom: 02. Dez. (DE-627)508725739 (DE-600)2224965-5 1748-717X nnns volume:17 year:2022 number:1 day:02 month:12 https://dx.doi.org/10.1186/s13014-022-02164-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 17 2022 1 02 12
language	English
source	Enthalten in Radiation oncology 17(2022), 1 vom: 02. Dez. volume:17 year:2022 number:1 day:02 month:12
sourceStr	Enthalten in Radiation oncology 17(2022), 1 vom: 02. Dez. volume:17 year:2022 number:1 day:02 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Quantitative image analysis Radiomics Survival analysis Glioblastoma Radiotherapy
isfreeaccess_bool	true
container_title	Radiation oncology
authorswithroles_txt_mv	Wiltgen, Tun @@aut@@ Fleischmann, Daniel F. @@aut@@ Kaiser, Lena @@aut@@ Holzgreve, Adrien @@aut@@ Corradini, Stefanie @@aut@@ Landry, Guillaume @@aut@@ Ingrisch, Michael @@aut@@ Popp, Ilinca @@aut@@ Grosu, Anca L. @@aut@@ Unterrainer, Marcus @@aut@@ Bartenstein, Peter @@aut@@ Parodi, Katia @@aut@@ Belka, Claus @@aut@@ Albert, Nathalie @@aut@@ Niyazi, Maximilian @@aut@@ Riboldi, Marco @@aut@@
publishDateDaySort_date	2022-12-02T00:00:00Z
hierarchy_top_id	508725739
id	SPR051197820
language_de	englisch
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In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. 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author	Wiltgen, Tun
spellingShingle	Wiltgen, Tun misc Quantitative image analysis misc Radiomics misc Survival analysis misc Glioblastoma misc Radiotherapy 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
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topic_title	18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy Quantitative image analysis (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Glioblastoma (dpeaa)DE-He213 Radiotherapy (dpeaa)DE-He213
topic	misc Quantitative image analysis misc Radiomics misc Survival analysis misc Glioblastoma misc Radiotherapy
topic_unstemmed	misc Quantitative image analysis misc Radiomics misc Survival analysis misc Glioblastoma misc Radiotherapy
topic_browse	misc Quantitative image analysis misc Radiomics misc Survival analysis misc Glioblastoma misc Radiotherapy
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title	18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
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title_full	18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
author_sort	Wiltgen, Tun
journal	Radiation oncology
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author_browse	Wiltgen, Tun Fleischmann, Daniel F. Kaiser, Lena Holzgreve, Adrien Corradini, Stefanie Landry, Guillaume Ingrisch, Michael Popp, Ilinca Grosu, Anca L. Unterrainer, Marcus Bartenstein, Peter Parodi, Katia Belka, Claus Albert, Nathalie Niyazi, Maximilian Riboldi, Marco
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author-letter	Wiltgen, Tun
doi_str_mv	10.1186/s13014-022-02164-6
title_sort	18f-fet pet radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
title_auth	18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
abstract	Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. © The Author(s) 2022
abstractGer	Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. © The Author(s) 2022
abstract_unstemmed	Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. © The Author(s) 2022
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title_short	18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy
url	https://dx.doi.org/10.1186/s13014-022-02164-6
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author2	Fleischmann, Daniel F. Kaiser, Lena Holzgreve, Adrien Corradini, Stefanie Landry, Guillaume Ingrisch, Michael Popp, Ilinca Grosu, Anca L. Unterrainer, Marcus Bartenstein, Peter Parodi, Katia Belka, Claus Albert, Nathalie Niyazi, Maximilian Riboldi, Marco
author2Str	Fleischmann, Daniel F. Kaiser, Lena Holzgreve, Adrien Corradini, Stefanie Landry, Guillaume Ingrisch, Michael Popp, Ilinca Grosu, Anca L. Unterrainer, Marcus Bartenstein, Peter Parodi, Katia Belka, Claus Albert, Nathalie Niyazi, Maximilian Riboldi, Marco
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In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. 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