Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning

Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ubaldi, Leonardo [verfasserIn] Saponaro, Sara [verfasserIn] Giuliano, Alessia [verfasserIn] Talamonti, Cinzia [verfasserIn] Retico, Alessandra [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization

Übergeordnetes Werk:	Enthalten in: Physica medica - Amsterdam : Elsevier, 1996, 107
Übergeordnetes Werk:	volume:107

DOI / URN:	10.1016/j.ejmp.2023.102538

Katalog-ID:	ELV064296822

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245	1	0	\|a Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
264		1	\|c 2023
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520			\|a Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
650		4	\|a Radiomics
650		4	\|a Machine learning
650		4	\|a Magnetic resonance imaging
650		4	\|a Glioma grading
650		4	\|a Robustness of features
650		4	\|a Image normalization
700	1		\|a Saponaro, Sara \|e verfasserin \|0 (orcid)0000-0003-3930-3976 \|4 aut
700	1		\|a Giuliano, Alessia \|e verfasserin \|0 (orcid)0000-0002-7381-078X \|4 aut
700	1		\|a Talamonti, Cinzia \|e verfasserin \|0 (orcid)0000-0003-2955-6451 \|4 aut
700	1		\|a Retico, Alessandra \|e verfasserin \|0 (orcid)0000-0001-5135-4472 \|4 aut
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author_variant	l u lu s s ss a g ag c t ct a r ar
matchkey_str	article:1724191X:2023----::eiiguniaienomtofomliaaercrvaaimceautooteoutesnpeitvvlefaimcetrsnhdsrmntoo
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publishDate	2023
allfields	10.1016/j.ejmp.2023.102538 doi (DE-627)ELV064296822 (ELSEVIER)S1120-1797(23)00015-7 DE-627 ger DE-627 rda eng 530 610 VZ 44.31 bkl Ubaldi, Leonardo verfasserin (orcid)0000-0002-3446-1636 aut Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization Saponaro, Sara verfasserin (orcid)0000-0003-3930-3976 aut Giuliano, Alessia verfasserin (orcid)0000-0002-7381-078X aut Talamonti, Cinzia verfasserin (orcid)0000-0003-2955-6451 aut Retico, Alessandra verfasserin (orcid)0000-0001-5135-4472 aut Enthalten in Physica medica Amsterdam : Elsevier, 1996 107 Online-Ressource (DE-627)364471417 (DE-600)2110535-2 (DE-576)272350176 1724-191X nnns volume:107 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.31 Medizinische Physik VZ AR 107
spelling	10.1016/j.ejmp.2023.102538 doi (DE-627)ELV064296822 (ELSEVIER)S1120-1797(23)00015-7 DE-627 ger DE-627 rda eng 530 610 VZ 44.31 bkl Ubaldi, Leonardo verfasserin (orcid)0000-0002-3446-1636 aut Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization Saponaro, Sara verfasserin (orcid)0000-0003-3930-3976 aut Giuliano, Alessia verfasserin (orcid)0000-0002-7381-078X aut Talamonti, Cinzia verfasserin (orcid)0000-0003-2955-6451 aut Retico, Alessandra verfasserin (orcid)0000-0001-5135-4472 aut Enthalten in Physica medica Amsterdam : Elsevier, 1996 107 Online-Ressource (DE-627)364471417 (DE-600)2110535-2 (DE-576)272350176 1724-191X nnns volume:107 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.31 Medizinische Physik VZ AR 107
allfields_unstemmed	10.1016/j.ejmp.2023.102538 doi (DE-627)ELV064296822 (ELSEVIER)S1120-1797(23)00015-7 DE-627 ger DE-627 rda eng 530 610 VZ 44.31 bkl Ubaldi, Leonardo verfasserin (orcid)0000-0002-3446-1636 aut Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization Saponaro, Sara verfasserin (orcid)0000-0003-3930-3976 aut Giuliano, Alessia verfasserin (orcid)0000-0002-7381-078X aut Talamonti, Cinzia verfasserin (orcid)0000-0003-2955-6451 aut Retico, Alessandra verfasserin (orcid)0000-0001-5135-4472 aut Enthalten in Physica medica Amsterdam : Elsevier, 1996 107 Online-Ressource (DE-627)364471417 (DE-600)2110535-2 (DE-576)272350176 1724-191X nnns volume:107 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.31 Medizinische Physik VZ AR 107
allfieldsGer	10.1016/j.ejmp.2023.102538 doi (DE-627)ELV064296822 (ELSEVIER)S1120-1797(23)00015-7 DE-627 ger DE-627 rda eng 530 610 VZ 44.31 bkl Ubaldi, Leonardo verfasserin (orcid)0000-0002-3446-1636 aut Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization Saponaro, Sara verfasserin (orcid)0000-0003-3930-3976 aut Giuliano, Alessia verfasserin (orcid)0000-0002-7381-078X aut Talamonti, Cinzia verfasserin (orcid)0000-0003-2955-6451 aut Retico, Alessandra verfasserin (orcid)0000-0001-5135-4472 aut Enthalten in Physica medica Amsterdam : Elsevier, 1996 107 Online-Ressource (DE-627)364471417 (DE-600)2110535-2 (DE-576)272350176 1724-191X nnns volume:107 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.31 Medizinische Physik VZ AR 107
allfieldsSound	10.1016/j.ejmp.2023.102538 doi (DE-627)ELV064296822 (ELSEVIER)S1120-1797(23)00015-7 DE-627 ger DE-627 rda eng 530 610 VZ 44.31 bkl Ubaldi, Leonardo verfasserin (orcid)0000-0002-3446-1636 aut Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out. Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization Saponaro, Sara verfasserin (orcid)0000-0003-3930-3976 aut Giuliano, Alessia verfasserin (orcid)0000-0002-7381-078X aut Talamonti, Cinzia verfasserin (orcid)0000-0003-2955-6451 aut Retico, Alessandra verfasserin (orcid)0000-0001-5135-4472 aut Enthalten in Physica medica Amsterdam : Elsevier, 1996 107 Online-Ressource (DE-627)364471417 (DE-600)2110535-2 (DE-576)272350176 1724-191X nnns volume:107 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.31 Medizinische Physik VZ AR 107
language	English
source	Enthalten in Physica medica 107 volume:107
sourceStr	Enthalten in Physica medica 107 volume:107
format_phy_str_mv	Article
bklname	Medizinische Physik
institution	findex.gbv.de
topic_facet	Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization
dewey-raw	530
isfreeaccess_bool	false
container_title	Physica medica
authorswithroles_txt_mv	Ubaldi, Leonardo @@aut@@ Saponaro, Sara @@aut@@ Giuliano, Alessia @@aut@@ Talamonti, Cinzia @@aut@@ Retico, Alessandra @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	364471417
dewey-sort	3530
id	ELV064296822
language_de	englisch
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This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. 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author	Ubaldi, Leonardo
spellingShingle	Ubaldi, Leonardo ddc 530 bkl 44.31 misc Radiomics misc Machine learning misc Magnetic resonance imaging misc Glioma grading misc Robustness of features misc Image normalization Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
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topic_title	530 610 VZ 44.31 bkl Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning Radiomics Machine learning Magnetic resonance imaging Glioma grading Robustness of features Image normalization
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title	Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
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title_full	Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
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author_browse	Ubaldi, Leonardo Saponaro, Sara Giuliano, Alessia Talamonti, Cinzia Retico, Alessandra
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title_sort	deriving quantitative information from multiparametric mri via radiomics: evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
title_auth	Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
abstract	Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
abstractGer	Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
abstract_unstemmed	Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
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title_short	Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning
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author2	Saponaro, Sara Giuliano, Alessia Talamonti, Cinzia Retico, Alessandra
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doi_str	10.1016/j.ejmp.2023.102538
up_date	2024-07-06T20:20:32.014Z
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This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings.Results: The results show that using MRI-reliable features improves the performance in glioma grade classification ( AUC = 0.93 ± 0.05 ) with respect to the use of raw ( AUC = 0.88 ± 0.08 ) and robust features ( AUC = 0.83 ± 0.08 ), defined as those not depending on image normalization and intensity discretization.Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. 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Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning

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