Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructe...
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Autor*in:	Wang, Bingzhen [verfasserIn] Liu, Jinghua Zhang, Xiaolei Wang, Zhongxiao Cao, Zhendong Lu, Lijun Lv, Wenbing Wang, Aihui Li, Shuyan Wu, Xiaotian Dong, Xianling

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Radiomics Dosiomics DVH Prognosis PET/CT Head and neck cancer

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: EJNMMI Research - Berlin : Springer, 2011, 13(2023), 1 vom: 13. Feb.
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:1 ; day:13 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s13550-023-00959-6

Katalog-ID:	SPR049324438

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520			\|a Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.
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allfields	10.1186/s13550-023-00959-6 doi (DE-627)SPR049324438 (SPR)s13550-023-00959-6-e DE-627 ger DE-627 rakwb eng Wang, Bingzhen verfasserin aut Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213 Liu, Jinghua aut Zhang, Xiaolei aut Wang, Zhongxiao aut Cao, Zhendong aut Lu, Lijun aut Lv, Wenbing aut Wang, Aihui aut Li, Shuyan aut Wu, Xiaotian aut Dong, Xianling (orcid)0000-0001-8572-2632 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 13. Feb. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:13 month:02 https://dx.doi.org/10.1186/s13550-023-00959-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 13 02
spelling	10.1186/s13550-023-00959-6 doi (DE-627)SPR049324438 (SPR)s13550-023-00959-6-e DE-627 ger DE-627 rakwb eng Wang, Bingzhen verfasserin aut Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213 Liu, Jinghua aut Zhang, Xiaolei aut Wang, Zhongxiao aut Cao, Zhendong aut Lu, Lijun aut Lv, Wenbing aut Wang, Aihui aut Li, Shuyan aut Wu, Xiaotian aut Dong, Xianling (orcid)0000-0001-8572-2632 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 13. Feb. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:13 month:02 https://dx.doi.org/10.1186/s13550-023-00959-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 13 02
allfields_unstemmed	10.1186/s13550-023-00959-6 doi (DE-627)SPR049324438 (SPR)s13550-023-00959-6-e DE-627 ger DE-627 rakwb eng Wang, Bingzhen verfasserin aut Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213 Liu, Jinghua aut Zhang, Xiaolei aut Wang, Zhongxiao aut Cao, Zhendong aut Lu, Lijun aut Lv, Wenbing aut Wang, Aihui aut Li, Shuyan aut Wu, Xiaotian aut Dong, Xianling (orcid)0000-0001-8572-2632 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 13. Feb. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:13 month:02 https://dx.doi.org/10.1186/s13550-023-00959-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 13 02
allfieldsGer	10.1186/s13550-023-00959-6 doi (DE-627)SPR049324438 (SPR)s13550-023-00959-6-e DE-627 ger DE-627 rakwb eng Wang, Bingzhen verfasserin aut Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213 Liu, Jinghua aut Zhang, Xiaolei aut Wang, Zhongxiao aut Cao, Zhendong aut Lu, Lijun aut Lv, Wenbing aut Wang, Aihui aut Li, Shuyan aut Wu, Xiaotian aut Dong, Xianling (orcid)0000-0001-8572-2632 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 13. Feb. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:13 month:02 https://dx.doi.org/10.1186/s13550-023-00959-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 13 02
allfieldsSound	10.1186/s13550-023-00959-6 doi (DE-627)SPR049324438 (SPR)s13550-023-00959-6-e DE-627 ger DE-627 rakwb eng Wang, Bingzhen verfasserin aut Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213 Liu, Jinghua aut Zhang, Xiaolei aut Wang, Zhongxiao aut Cao, Zhendong aut Lu, Lijun aut Lv, Wenbing aut Wang, Aihui aut Li, Shuyan aut Wu, Xiaotian aut Dong, Xianling (orcid)0000-0001-8572-2632 aut Enthalten in EJNMMI Research Berlin : Springer, 2011 13(2023), 1 vom: 13. Feb. (DE-627)664970265 (DE-600)2619892-7 2191-219X nnns volume:13 year:2023 number:1 day:13 month:02 https://dx.doi.org/10.1186/s13550-023-00959-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 13 02
language	English
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author	Wang, Bingzhen
spellingShingle	Wang, Bingzhen misc Radiomics misc Dosiomics misc DVH misc Prognosis misc PET/CT misc Head and neck cancer Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
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topic_title	Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer Radiomics (dpeaa)DE-He213 Dosiomics (dpeaa)DE-He213 DVH (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Head and neck cancer (dpeaa)DE-He213
topic	misc Radiomics misc Dosiomics misc DVH misc Prognosis misc PET/CT misc Head and neck cancer
topic_unstemmed	misc Radiomics misc Dosiomics misc DVH misc Prognosis misc PET/CT misc Head and neck cancer
topic_browse	misc Radiomics misc Dosiomics misc DVH misc Prognosis misc PET/CT misc Head and neck cancer
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title	Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
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title_full	Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
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author_browse	Wang, Bingzhen Liu, Jinghua Zhang, Xiaolei Wang, Zhongxiao Cao, Zhendong Lu, Lijun Lv, Wenbing Wang, Aihui Li, Shuyan Wu, Xiaotian Dong, Xianling
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title_sort	prognostic value of 18f-fdg pet/ct-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
title_auth	Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
abstract	Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. © The Author(s) 2023
abstractGer	Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. © The Author(s) 2023
abstract_unstemmed	Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. © The Author(s) 2023
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title_short	Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer
url	https://dx.doi.org/10.1186/s13550-023-00959-6
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author2	Liu, Jinghua Zhang, Xiaolei Wang, Zhongxiao Cao, Zhendong Lu, Lijun Lv, Wenbing Wang, Aihui Li, Shuyan Wu, Xiaotian Dong, Xianling
author2Str	Liu, Jinghua Zhang, Xiaolei Wang, Zhongxiao Cao, Zhendong Lu, Lijun Lv, Wenbing Wang, Aihui Li, Shuyan Wu, Xiaotian Dong, Xianling
ppnlink	664970265
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doi_str	10.1186/s13550-023-00959-6
up_date	2024-07-04T00:20:17.515Z
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Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). 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Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

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