Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy
BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and gui...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
De-Zhen Guo [verfasserIn] Shi-Yu Zhang [verfasserIn] San-Yuan Dong [verfasserIn] Jia-Yan Yan [verfasserIn] Yu-Peng Wang [verfasserIn] Ya Cao [verfasserIn] Sheng-Xiang Rao [verfasserIn] Jia Fan [verfasserIn] Xin-Rong Yang [verfasserIn] Ao Huang [verfasserIn] Jian Zhou [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Frontiers in Immunology - Frontiers Media S.A., 2011, 14(2023) |
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| Übergeordnetes Werk: |
volume:14 ; year:2023 |
| Links: |
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| DOI / URN: |
10.3389/fimmu.2023.1141199 |
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| Katalog-ID: |
DOAJ081946376 |
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| 245 | 1 | 0 | |a Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy |
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| 520 | |a BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. | ||
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| 700 | 0 | |a Jia-Yan Yan |e verfasserin |4 aut | |
| 700 | 0 | |a Jia-Yan Yan |e verfasserin |4 aut | |
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10.3389/fimmu.2023.1141199 doi (DE-627)DOAJ081946376 (DE-599)DOAJ56079ece69b04b749806519d91bd529f DE-627 ger DE-627 rakwb eng RC581-607 De-Zhen Guo verfasserin aut Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II Immunologic diseases. Allergy De-Zhen Guo verfasserin aut De-Zhen Guo verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut San-Yuan Dong verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Ya Cao verfasserin aut Ya Cao verfasserin aut Sheng-Xiang Rao verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 14(2023) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:14 year:2023 https://doi.org/10.3389/fimmu.2023.1141199 kostenfrei https://doaj.org/article/56079ece69b04b749806519d91bd529f kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full kostenfrei https://doaj.org/toc/1664-3224 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 14 2023 |
| spelling |
10.3389/fimmu.2023.1141199 doi (DE-627)DOAJ081946376 (DE-599)DOAJ56079ece69b04b749806519d91bd529f DE-627 ger DE-627 rakwb eng RC581-607 De-Zhen Guo verfasserin aut Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II Immunologic diseases. Allergy De-Zhen Guo verfasserin aut De-Zhen Guo verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut San-Yuan Dong verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Ya Cao verfasserin aut Ya Cao verfasserin aut Sheng-Xiang Rao verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 14(2023) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:14 year:2023 https://doi.org/10.3389/fimmu.2023.1141199 kostenfrei https://doaj.org/article/56079ece69b04b749806519d91bd529f kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full kostenfrei https://doaj.org/toc/1664-3224 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 14 2023 |
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10.3389/fimmu.2023.1141199 doi (DE-627)DOAJ081946376 (DE-599)DOAJ56079ece69b04b749806519d91bd529f DE-627 ger DE-627 rakwb eng RC581-607 De-Zhen Guo verfasserin aut Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II Immunologic diseases. Allergy De-Zhen Guo verfasserin aut De-Zhen Guo verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut San-Yuan Dong verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Ya Cao verfasserin aut Ya Cao verfasserin aut Sheng-Xiang Rao verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 14(2023) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:14 year:2023 https://doi.org/10.3389/fimmu.2023.1141199 kostenfrei https://doaj.org/article/56079ece69b04b749806519d91bd529f kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full kostenfrei https://doaj.org/toc/1664-3224 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 14 2023 |
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10.3389/fimmu.2023.1141199 doi (DE-627)DOAJ081946376 (DE-599)DOAJ56079ece69b04b749806519d91bd529f DE-627 ger DE-627 rakwb eng RC581-607 De-Zhen Guo verfasserin aut Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II Immunologic diseases. Allergy De-Zhen Guo verfasserin aut De-Zhen Guo verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut San-Yuan Dong verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Ya Cao verfasserin aut Ya Cao verfasserin aut Sheng-Xiang Rao verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 14(2023) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:14 year:2023 https://doi.org/10.3389/fimmu.2023.1141199 kostenfrei https://doaj.org/article/56079ece69b04b749806519d91bd529f kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full kostenfrei https://doaj.org/toc/1664-3224 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 14 2023 |
| allfieldsSound |
10.3389/fimmu.2023.1141199 doi (DE-627)DOAJ081946376 (DE-599)DOAJ56079ece69b04b749806519d91bd529f DE-627 ger DE-627 rakwb eng RC581-607 De-Zhen Guo verfasserin aut Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II Immunologic diseases. Allergy De-Zhen Guo verfasserin aut De-Zhen Guo verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut Shi-Yu Zhang verfasserin aut San-Yuan Dong verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Jia-Yan Yan verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Yu-Peng Wang verfasserin aut Ya Cao verfasserin aut Ya Cao verfasserin aut Sheng-Xiang Rao verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Jia Fan verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Xin-Rong Yang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Ao Huang verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut Jian Zhou verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 14(2023) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:14 year:2023 https://doi.org/10.3389/fimmu.2023.1141199 kostenfrei https://doaj.org/article/56079ece69b04b749806519d91bd529f kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full kostenfrei https://doaj.org/toc/1664-3224 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 14 2023 |
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De-Zhen Guo @@aut@@ Shi-Yu Zhang @@aut@@ San-Yuan Dong @@aut@@ Jia-Yan Yan @@aut@@ Yu-Peng Wang @@aut@@ Ya Cao @@aut@@ Sheng-Xiang Rao @@aut@@ Jia Fan @@aut@@ Xin-Rong Yang @@aut@@ Ao Huang @@aut@@ Jian Zhou @@aut@@ |
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However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P&lt;0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II&lt;600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II&gt;600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II&gt;600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. 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RC581-607 Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy predicting model liver cancer lenvatinib immunotherapy protein induced by vitamin K absence or antagonist-II |
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Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy |
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prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy |
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Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy |
| abstract |
BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. |
| abstractGer |
BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. |
| abstract_unstemmed |
BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients. |
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Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy |
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Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. 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