Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with tr...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bin-Yan Zhong [verfasserIn] Zhi-Ping Yan [verfasserIn] Jun-Hui Sun [verfasserIn] Lei Zhang [verfasserIn] Zhong-Heng Hou [verfasserIn] Min-Jie Yang [verfasserIn] Guan-Hui Zhou [verfasserIn] Wan-Sheng Wang [verfasserIn] Zhi Li [verfasserIn] Peng Huang [verfasserIn] Shen Zhang [verfasserIn] Xiao-Li Zhu [verfasserIn] Cai-Fang Ni [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network

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.2020.525461

Katalog-ID:	DOAJ055092020

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ055092020
003	DE-627
005	20230308185552.0
007	cr uuu---uuuuu
008	230227s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3389/fonc.2020.525461 \|2 doi
035			\|a (DE-627)DOAJ055092020
035			\|a (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a RC254-282
100	0		\|a Bin-Yan Zhong \|e verfasserin \|4 aut
245	1	0	\|a Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.
650		4	\|a hepatocellular carcinoma
650		4	\|a albumin–bilirubin
650		4	\|a artificial intelligence
650		4	\|a nomogram
650		4	\|a artificial neural network
653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
700	0		\|a Zhi-Ping Yan \|e verfasserin \|4 aut
700	0		\|a Zhi-Ping Yan \|e verfasserin \|4 aut
700	0		\|a Zhi-Ping Yan \|e verfasserin \|4 aut
700	0		\|a Jun-Hui Sun \|e verfasserin \|4 aut
700	0		\|a Lei Zhang \|e verfasserin \|4 aut
700	0		\|a Zhong-Heng Hou \|e verfasserin \|4 aut
700	0		\|a Min-Jie Yang \|e verfasserin \|4 aut
700	0		\|a Min-Jie Yang \|e verfasserin \|4 aut
700	0		\|a Min-Jie Yang \|e verfasserin \|4 aut
700	0		\|a Guan-Hui Zhou \|e verfasserin \|4 aut
700	0		\|a Wan-Sheng Wang \|e verfasserin \|4 aut
700	0		\|a Zhi Li \|e verfasserin \|4 aut
700	0		\|a Peng Huang \|e verfasserin \|4 aut
700	0		\|a Shen Zhang \|e verfasserin \|4 aut
700	0		\|a Xiao-Li Zhu \|e verfasserin \|4 aut
700	0		\|a Cai-Fang Ni \|e verfasserin \|4 aut
773	0	8	\|i In \|t Frontiers in Oncology \|d Frontiers Media S.A., 2012 \|g 10(2020) \|w (DE-627)684965518 \|w (DE-600)2649216-7 \|x 2234943X \|7 nnns
773	1	8	\|g volume:10 \|g year:2020
856	4	0	\|u https://doi.org/10.3389/fonc.2020.525461 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce \|z kostenfrei
856	4	0	\|u https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2234-943X \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2020

Indexfelder

author_variant	b y z byz z p y zpy z p y zpy z p y zpy j h s jhs l z lz z h h zhh m j y mjy m j y mjy m j y mjy g h z ghz w s w wsw z l zl p h ph s z sz x l z xlz c f n cfn
matchkey_str	article:2234943X:2020----::rgotcefracoabmniiuigaeihriiilnelgneohptcluacrioaraewttasre
hierarchy_sort_str	2020
callnumber-subject-code	RC
publishDate	2020
allfields	10.3389/fonc.2020.525461 doi (DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce DE-627 ger DE-627 rakwb eng RC254-282 Bin-Yan Zhong verfasserin aut Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy. hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Jun-Hui Sun verfasserin aut Lei Zhang verfasserin aut Zhong-Heng Hou verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Guan-Hui Zhou verfasserin aut Wan-Sheng Wang verfasserin aut Zhi Li verfasserin aut Peng Huang verfasserin aut Shen Zhang verfasserin aut Xiao-Li Zhu verfasserin aut Cai-Fang Ni 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.2020.525461 kostenfrei https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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.2020.525461 doi (DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce DE-627 ger DE-627 rakwb eng RC254-282 Bin-Yan Zhong verfasserin aut Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy. hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Jun-Hui Sun verfasserin aut Lei Zhang verfasserin aut Zhong-Heng Hou verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Guan-Hui Zhou verfasserin aut Wan-Sheng Wang verfasserin aut Zhi Li verfasserin aut Peng Huang verfasserin aut Shen Zhang verfasserin aut Xiao-Li Zhu verfasserin aut Cai-Fang Ni 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.2020.525461 kostenfrei https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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.2020.525461 doi (DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce DE-627 ger DE-627 rakwb eng RC254-282 Bin-Yan Zhong verfasserin aut Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy. hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Jun-Hui Sun verfasserin aut Lei Zhang verfasserin aut Zhong-Heng Hou verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Guan-Hui Zhou verfasserin aut Wan-Sheng Wang verfasserin aut Zhi Li verfasserin aut Peng Huang verfasserin aut Shen Zhang verfasserin aut Xiao-Li Zhu verfasserin aut Cai-Fang Ni 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.2020.525461 kostenfrei https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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.2020.525461 doi (DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce DE-627 ger DE-627 rakwb eng RC254-282 Bin-Yan Zhong verfasserin aut Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy. hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Jun-Hui Sun verfasserin aut Lei Zhang verfasserin aut Zhong-Heng Hou verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Guan-Hui Zhou verfasserin aut Wan-Sheng Wang verfasserin aut Zhi Li verfasserin aut Peng Huang verfasserin aut Shen Zhang verfasserin aut Xiao-Li Zhu verfasserin aut Cai-Fang Ni 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.2020.525461 kostenfrei https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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.2020.525461 doi (DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce DE-627 ger DE-627 rakwb eng RC254-282 Bin-Yan Zhong verfasserin aut Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy. hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Zhi-Ping Yan verfasserin aut Jun-Hui Sun verfasserin aut Lei Zhang verfasserin aut Zhong-Heng Hou verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Min-Jie Yang verfasserin aut Guan-Hui Zhou verfasserin aut Wan-Sheng Wang verfasserin aut Zhi Li verfasserin aut Peng Huang verfasserin aut Shen Zhang verfasserin aut Xiao-Li Zhu verfasserin aut Cai-Fang Ni 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.2020.525461 kostenfrei https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Frontiers in Oncology
authorswithroles_txt_mv	Bin-Yan Zhong @@aut@@ Zhi-Ping Yan @@aut@@ Jun-Hui Sun @@aut@@ Lei Zhang @@aut@@ Zhong-Heng Hou @@aut@@ Min-Jie Yang @@aut@@ Guan-Hui Zhou @@aut@@ Wan-Sheng Wang @@aut@@ Zhi Li @@aut@@ Peng Huang @@aut@@ Shen Zhang @@aut@@ Xiao-Li Zhu @@aut@@ Cai-Fang Ni @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	684965518
id	DOAJ055092020
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ055092020</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308185552.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fonc.2020.525461</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ055092020</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Bin-Yan Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hepatocellular carcinoma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">albumin–bilirubin</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nomogram</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial neural network</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. Including cancer and carcinogens</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jun-Hui Sun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhong-Heng Hou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guan-Hui Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wan-Sheng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peng Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shen Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao-Li Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cai-Fang Ni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Oncology</subfield><subfield code="d">Frontiers Media S.A., 2012</subfield><subfield code="g">10(2020)</subfield><subfield code="w">(DE-627)684965518</subfield><subfield code="w">(DE-600)2649216-7</subfield><subfield code="x">2234943X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2020</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fonc.2020.525461</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2234-943X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2020</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Bin-Yan Zhong
spellingShingle	Bin-Yan Zhong misc RC254-282 misc hepatocellular carcinoma misc albumin–bilirubin misc artificial intelligence misc nomogram misc artificial neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
authorStr	Bin-Yan Zhong
ppnlink_with_tag_str_mv	@@773@@(DE-627)684965518
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	RC254-282
illustrated	Not Illustrated
issn	2234943X
topic_title	RC254-282 Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib hepatocellular carcinoma albumin–bilirubin artificial intelligence nomogram artificial neural network
topic	misc RC254-282 misc hepatocellular carcinoma misc albumin–bilirubin misc artificial intelligence misc nomogram misc artificial neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc hepatocellular carcinoma misc albumin–bilirubin misc artificial intelligence misc nomogram misc artificial neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc hepatocellular carcinoma misc albumin–bilirubin misc artificial intelligence misc nomogram misc artificial neural network misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Frontiers in Oncology
hierarchy_parent_id	684965518
hierarchy_top_title	Frontiers in Oncology
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)684965518 (DE-600)2649216-7
title	Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
ctrlnum	(DE-627)DOAJ055092020 (DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce
title_full	Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
author_sort	Bin-Yan Zhong
journal	Frontiers in Oncology
journalStr	Frontiers in Oncology
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
author_browse	Bin-Yan Zhong Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Min-Jie Yang Guan-Hui Zhou Wan-Sheng Wang Zhi Li Peng Huang Shen Zhang Xiao-Li Zhu Cai-Fang Ni
container_volume	10
class	RC254-282
format_se	Elektronische Aufsätze
author-letter	Bin-Yan Zhong
doi_str_mv	10.3389/fonc.2020.525461
author2-role	verfasserin
title_sort	prognostic performance of albumin–bilirubin grade with artificial intelligence for hepatocellular carcinoma treated with transarterial chemoembolization combined with sorafenib
callnumber	RC254-282
title_auth	Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
abstract	PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.
abstractGer	PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.
abstract_unstemmed	PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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
title_short	Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib
url	https://doi.org/10.3389/fonc.2020.525461 https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full https://doaj.org/toc/2234-943X
remote_bool	true
author2	Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Min-Jie Yang Guan-Hui Zhou Wan-Sheng Wang Zhi Li Peng Huang Shen Zhang Xiao-Li Zhu Cai-Fang Ni
author2Str	Zhi-Ping Yan Jun-Hui Sun Lei Zhang Zhong-Heng Hou Min-Jie Yang Guan-Hui Zhou Wan-Sheng Wang Zhi Li Peng Huang Shen Zhang Xiao-Li Zhu Cai-Fang Ni
ppnlink	684965518
callnumber-subject	RC - Internal Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3389/fonc.2020.525461
callnumber-a	RC254-282
up_date	2024-07-04T01:46:20.227Z
_version_	1803611097246203904
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ055092020</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308185552.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fonc.2020.525461</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ055092020</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ2f583820831e48d9957d5c98d33b2cce</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Bin-Yan Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hepatocellular carcinoma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">albumin–bilirubin</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nomogram</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial neural network</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. Including cancer and carcinogens</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi-Ping Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jun-Hui Sun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhong-Heng Hou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Min-Jie Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guan-Hui Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wan-Sheng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhi Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Peng Huang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shen Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiao-Li Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Cai-Fang Ni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Oncology</subfield><subfield code="d">Frontiers Media S.A., 2012</subfield><subfield code="g">10(2020)</subfield><subfield code="w">(DE-627)684965518</subfield><subfield code="w">(DE-600)2649216-7</subfield><subfield code="x">2234943X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2020</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fonc.2020.525461</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/2f583820831e48d9957d5c98d33b2cce</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/articles/10.3389/fonc.2020.525461/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2234-943X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2020</subfield></datafield></record></collection>
score	7.402112

Nicht das Richtige dabei?

Schreiben Sie uns!

Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?