Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT

Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lihong Peng [verfasserIn] Hui Xu [verfasserIn] Wenbing Lv [verfasserIn] Lijun Lu [verfasserIn] Wufan Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	radiomics PET/CT robustness texture feature

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 15(2023), 3, p 932
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:3, p 932

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers15030932

Katalog-ID:	DOAJ080672574

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520			\|a Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.3390/cancers15030932 doi (DE-627)DOAJ080672574 (DE-599)DOAJ1de76e0aca144abdb08403bfce75859b DE-627 ger DE-627 rakwb eng RC254-282 Lihong Peng verfasserin aut Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features. radiomics PET/CT robustness texture feature Neoplasms. Tumors. Oncology. Including cancer and carcinogens Hui Xu verfasserin aut Wenbing Lv verfasserin aut Lijun Lu verfasserin aut Wufan Chen verfasserin aut In Cancers MDPI AG, 2010 15(2023), 3, p 932 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:3, p 932 https://doi.org/10.3390/cancers15030932 kostenfrei https://doaj.org/article/1de76e0aca144abdb08403bfce75859b kostenfrei https://www.mdpi.com/2072-6694/15/3/932 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 15 2023 3, p 932
spelling	10.3390/cancers15030932 doi (DE-627)DOAJ080672574 (DE-599)DOAJ1de76e0aca144abdb08403bfce75859b DE-627 ger DE-627 rakwb eng RC254-282 Lihong Peng verfasserin aut Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features. radiomics PET/CT robustness texture feature Neoplasms. Tumors. Oncology. Including cancer and carcinogens Hui Xu verfasserin aut Wenbing Lv verfasserin aut Lijun Lu verfasserin aut Wufan Chen verfasserin aut In Cancers MDPI AG, 2010 15(2023), 3, p 932 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:3, p 932 https://doi.org/10.3390/cancers15030932 kostenfrei https://doaj.org/article/1de76e0aca144abdb08403bfce75859b kostenfrei https://www.mdpi.com/2072-6694/15/3/932 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 15 2023 3, p 932
allfields_unstemmed	10.3390/cancers15030932 doi (DE-627)DOAJ080672574 (DE-599)DOAJ1de76e0aca144abdb08403bfce75859b DE-627 ger DE-627 rakwb eng RC254-282 Lihong Peng verfasserin aut Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features. radiomics PET/CT robustness texture feature Neoplasms. Tumors. Oncology. Including cancer and carcinogens Hui Xu verfasserin aut Wenbing Lv verfasserin aut Lijun Lu verfasserin aut Wufan Chen verfasserin aut In Cancers MDPI AG, 2010 15(2023), 3, p 932 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:3, p 932 https://doi.org/10.3390/cancers15030932 kostenfrei https://doaj.org/article/1de76e0aca144abdb08403bfce75859b kostenfrei https://www.mdpi.com/2072-6694/15/3/932 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 15 2023 3, p 932
allfieldsGer	10.3390/cancers15030932 doi (DE-627)DOAJ080672574 (DE-599)DOAJ1de76e0aca144abdb08403bfce75859b DE-627 ger DE-627 rakwb eng RC254-282 Lihong Peng verfasserin aut Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features. radiomics PET/CT robustness texture feature Neoplasms. Tumors. Oncology. Including cancer and carcinogens Hui Xu verfasserin aut Wenbing Lv verfasserin aut Lijun Lu verfasserin aut Wufan Chen verfasserin aut In Cancers MDPI AG, 2010 15(2023), 3, p 932 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:3, p 932 https://doi.org/10.3390/cancers15030932 kostenfrei https://doaj.org/article/1de76e0aca144abdb08403bfce75859b kostenfrei https://www.mdpi.com/2072-6694/15/3/932 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 15 2023 3, p 932
allfieldsSound	10.3390/cancers15030932 doi (DE-627)DOAJ080672574 (DE-599)DOAJ1de76e0aca144abdb08403bfce75859b DE-627 ger DE-627 rakwb eng RC254-282 Lihong Peng verfasserin aut Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features. radiomics PET/CT robustness texture feature Neoplasms. Tumors. Oncology. Including cancer and carcinogens Hui Xu verfasserin aut Wenbing Lv verfasserin aut Lijun Lu verfasserin aut Wufan Chen verfasserin aut In Cancers MDPI AG, 2010 15(2023), 3, p 932 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:3, p 932 https://doi.org/10.3390/cancers15030932 kostenfrei https://doaj.org/article/1de76e0aca144abdb08403bfce75859b kostenfrei https://www.mdpi.com/2072-6694/15/3/932 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 15 2023 3, p 932
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callnumber-first	R - Medicine
author	Lihong Peng
spellingShingle	Lihong Peng misc RC254-282 misc radiomics misc PET/CT misc robustness misc texture feature misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT
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topic_title	RC254-282 Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT radiomics PET/CT robustness texture feature
topic	misc RC254-282 misc radiomics misc PET/CT misc robustness misc texture feature misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc radiomics misc PET/CT misc robustness misc texture feature misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc radiomics misc PET/CT misc robustness misc texture feature misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT
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title_full	Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT
author_sort	Lihong Peng
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author_browse	Lihong Peng Hui Xu Wenbing Lv Lijun Lu Wufan Chen
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title_sort	impact of aggregation methods for texture features on their robustness performance: application to nasopharyngeal <sup<18</sup<f-fdg pet/ct
callnumber	RC254-282
title_auth	Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT
abstract	Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
abstractGer	Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
abstract_unstemmed	Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on <sup<18</sup<F-FDG PET/CT images. Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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title_short	Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT
url	https://doi.org/10.3390/cancers15030932 https://doaj.org/article/1de76e0aca144abdb08403bfce75859b https://www.mdpi.com/2072-6694/15/3/932 https://doaj.org/toc/2072-6694
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Results: There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Conclusion: Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. 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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal <sup<18</sup<F-FDG PET/CT

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