Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization

Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiang-Pan Meng [verfasserIn] Yuan-Cheng Wang [verfasserIn] Shenghong Ju [verfasserIn] Chun-Qiang Lu [verfasserIn] Bin-Yan Zhong [verfasserIn] Cai-Fang Ni [verfasserIn] Qi Zhang [verfasserIn] Qian Yu [verfasserIn] Jian Xu [verfasserIn] JianSong Ji [verfasserIn] Xiu-Ming Zhang [verfasserIn] Tian-Yu Tang [verfasserIn] Guanyu Yang [verfasserIn] Ziteng Zhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 10(2020)
Übergeordnetes Werk:	volume:10 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2020.01196

Katalog-ID:	DOAJ075516667

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650		4	\|a hepatocellular carcinomas
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650		4	\|a transarterial chemoembolization
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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allfields	10.3389/fonc.2020.01196 doi (DE-627)DOAJ075516667 (DE-599)DOAJbf9667b7f8f94b218b418e693455cdb3 DE-627 ger DE-627 rakwb eng RC254-282 Xiang-Pan Meng verfasserin aut Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yuan-Cheng Wang verfasserin aut Shenghong Ju verfasserin aut Chun-Qiang Lu verfasserin aut Bin-Yan Zhong verfasserin aut Cai-Fang Ni verfasserin aut Qi Zhang verfasserin aut Qian Yu verfasserin aut Jian Xu verfasserin aut JianSong Ji verfasserin aut Xiu-Ming Zhang verfasserin aut Tian-Yu Tang verfasserin aut Guanyu Yang verfasserin aut Ziteng Zhao verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.01196 kostenfrei https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 kostenfrei https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 10 2020
spelling	10.3389/fonc.2020.01196 doi (DE-627)DOAJ075516667 (DE-599)DOAJbf9667b7f8f94b218b418e693455cdb3 DE-627 ger DE-627 rakwb eng RC254-282 Xiang-Pan Meng verfasserin aut Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yuan-Cheng Wang verfasserin aut Shenghong Ju verfasserin aut Chun-Qiang Lu verfasserin aut Bin-Yan Zhong verfasserin aut Cai-Fang Ni verfasserin aut Qi Zhang verfasserin aut Qian Yu verfasserin aut Jian Xu verfasserin aut JianSong Ji verfasserin aut Xiu-Ming Zhang verfasserin aut Tian-Yu Tang verfasserin aut Guanyu Yang verfasserin aut Ziteng Zhao verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.01196 kostenfrei https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 kostenfrei https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 10 2020
allfields_unstemmed	10.3389/fonc.2020.01196 doi (DE-627)DOAJ075516667 (DE-599)DOAJbf9667b7f8f94b218b418e693455cdb3 DE-627 ger DE-627 rakwb eng RC254-282 Xiang-Pan Meng verfasserin aut Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yuan-Cheng Wang verfasserin aut Shenghong Ju verfasserin aut Chun-Qiang Lu verfasserin aut Bin-Yan Zhong verfasserin aut Cai-Fang Ni verfasserin aut Qi Zhang verfasserin aut Qian Yu verfasserin aut Jian Xu verfasserin aut JianSong Ji verfasserin aut Xiu-Ming Zhang verfasserin aut Tian-Yu Tang verfasserin aut Guanyu Yang verfasserin aut Ziteng Zhao verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.01196 kostenfrei https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 kostenfrei https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 10 2020
allfieldsGer	10.3389/fonc.2020.01196 doi (DE-627)DOAJ075516667 (DE-599)DOAJbf9667b7f8f94b218b418e693455cdb3 DE-627 ger DE-627 rakwb eng RC254-282 Xiang-Pan Meng verfasserin aut Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yuan-Cheng Wang verfasserin aut Shenghong Ju verfasserin aut Chun-Qiang Lu verfasserin aut Bin-Yan Zhong verfasserin aut Cai-Fang Ni verfasserin aut Qi Zhang verfasserin aut Qian Yu verfasserin aut Jian Xu verfasserin aut JianSong Ji verfasserin aut Xiu-Ming Zhang verfasserin aut Tian-Yu Tang verfasserin aut Guanyu Yang verfasserin aut Ziteng Zhao verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.01196 kostenfrei https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 kostenfrei https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 10 2020
allfieldsSound	10.3389/fonc.2020.01196 doi (DE-627)DOAJ075516667 (DE-599)DOAJbf9667b7f8f94b218b418e693455cdb3 DE-627 ger DE-627 rakwb eng RC254-282 Xiang-Pan Meng verfasserin aut Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance. hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yuan-Cheng Wang verfasserin aut Shenghong Ju verfasserin aut Chun-Qiang Lu verfasserin aut Bin-Yan Zhong verfasserin aut Cai-Fang Ni verfasserin aut Qi Zhang verfasserin aut Qian Yu verfasserin aut Jian Xu verfasserin aut JianSong Ji verfasserin aut Xiu-Ming Zhang verfasserin aut Tian-Yu Tang verfasserin aut Guanyu Yang verfasserin aut Ziteng Zhao verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 10(2020) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:10 year:2020 https://doi.org/10.3389/fonc.2020.01196 kostenfrei https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 kostenfrei https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 10 2020
language	English
source	In Frontiers in Oncology 10(2020) volume:10 year:2020
sourceStr	In Frontiers in Oncology 10(2020) volume:10 year:2020
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Frontiers in Oncology
authorswithroles_txt_mv	Xiang-Pan Meng @@aut@@ Yuan-Cheng Wang @@aut@@ Shenghong Ju @@aut@@ Chun-Qiang Lu @@aut@@ Bin-Yan Zhong @@aut@@ Cai-Fang Ni @@aut@@ Qi Zhang @@aut@@ Qian Yu @@aut@@ Jian Xu @@aut@@ JianSong Ji @@aut@@ Xiu-Ming Zhang @@aut@@ Tian-Yu Tang @@aut@@ Guanyu Yang @@aut@@ Ziteng Zhao @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	684965518
id	DOAJ075516667
language_de	englisch
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callnumber-first	R - Medicine
author	Xiang-Pan Meng
spellingShingle	Xiang-Pan Meng misc RC254-282 misc hepatocellular carcinomas misc image processing (computer-assisted) misc radiomics misc transarterial chemoembolization misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization
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topic_title	RC254-282 Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization hepatocellular carcinomas image processing (computer-assisted) radiomics transarterial chemoembolization biomarkers
topic	misc RC254-282 misc hepatocellular carcinomas misc image processing (computer-assisted) misc radiomics misc transarterial chemoembolization misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc hepatocellular carcinomas misc image processing (computer-assisted) misc radiomics misc transarterial chemoembolization misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc hepatocellular carcinomas misc image processing (computer-assisted) misc radiomics misc transarterial chemoembolization misc biomarkers misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization
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title_full	Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization
author_sort	Xiang-Pan Meng
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author_browse	Xiang-Pan Meng Yuan-Cheng Wang Shenghong Ju Chun-Qiang Lu Bin-Yan Zhong Cai-Fang Ni Qi Zhang Qian Yu Jian Xu JianSong Ji Xiu-Ming Zhang Tian-Yu Tang Guanyu Yang Ziteng Zhao
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title_sort	radiomics analysis on multiphase contrast-enhanced ct: a survival prediction tool in patients with hepatocellular carcinoma undergoing transarterial chemoembolization
callnumber	RC254-282
title_auth	Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization
abstract	Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.
abstractGer	Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.
abstract_unstemmed	Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.
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title_short	Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization
url	https://doi.org/10.3389/fonc.2020.01196 https://doaj.org/article/bf9667b7f8f94b218b418e693455cdb3 https://www.frontiersin.org/article/10.3389/fonc.2020.01196/full https://doaj.org/toc/2234-943X
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author2Str	Yuan-Cheng Wang Shenghong Ju Chun-Qiang Lu Bin-Yan Zhong Cai-Fang Ni Qi Zhang Qian Yu Jian Xu JianSong Ji Xiu-Ming Zhang Tian-Yu Tang Guanyu Yang Ziteng Zhao
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