The concept of therapeutic hierarchy for patients with hepatocellular carcinoma

A multicenter cohort study

Alessandro Vitale, Fabio Farinati, Timothy M. Pawlik, Anna Chiara Frigo, Edoardo G. Giannini, Lucia Napoli, Francesco Ciccarese, Gian Ludovico Rapaccini, Maria Di Marco, Eugenio Caturelli, Marco Zoli, Franco Borzio, Rodolfo Sacco, Giuseppe Cabibbo, Roberto Virdone, Fabio Marra, Martina Felder, Filomena Morisco, Luisa Benvegnù, Antonio Gasbarrini & 9 others Gianluca Svegliati-Baroni, Francesco Giuseppe Foschi, Gabriele Missale, Alberto Masotto, Gerardo Nardone, Antonio Colecchia, Mauro Bernardi, Franco Trevisani, Umberto Cillo

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: The Italian Liver Cancer (ITA.LI.CA) prognostic system for patients with hepatocellular carcinoma (HCC) has recently been proposed and validated. We sought to explore the relationship among the ITA.LI.CA prognostic variables (ie tumour stage, functional score based on performance status and Child-Pugh score, and alpha-fetoprotein), treatment selection and survival outcome in HCC patients. Patients and Methods: We analysed 4,867 consecutive HCC patients undergoing six main treatment strategies (liver transplantation, LT; liver resection, LR; ablation, ABL; intra-arterial therapy, IAT; Sorafenib, SOR; and best supportive care, BSC) and enrolled during 2002-2015 in a multicenter Italian database. In order to control pretreatment imbalances in observed variables, a machine learning methodology was used and inverse probability of treatment weights (IPTW) was calculated. An IPTW-adjusted multivariate survival model that included ITA.LI.CA prognostic variables, treatment period and treatment strategy was then developed. The survival benefit of HCC treatments was described as a hazard ratio (95% confidence interval), using BSC as a reference value and as predicted median survival. Results: After the IPTW, the six treatment groups became well balanced for most baseline characteristics. In the IPTW-adjusted multivariate survival model, treatment strategy was found to be the strongest survival predictor, irrespective of ITA.LI.CA prognostic variables and treatment period. The survival benefit of different therapies over BSC was: LT = 0.19 (0.18-0.20); RES = 0.40 (0.37-0.42); ABL 0.42 (0.40-0.44); IAT = 0.58 (0.55-0.61); SOR = 0.92 (0.87-0.97). This multivariate model was then used to predict median survival for each therapy within each ITA.LI.CA stage. Conclusion: The concept of therapeutic hierarchy was established within each ITA.LI.CA stage.

Original languageEnglish
JournalLiver International
DOIs
Publication statusPublished - Jan 1 2019

Fingerprint

Multicenter Studies
Hepatocellular Carcinoma
Cohort Studies
Survival
Therapeutics
Weights and Measures
alpha-Fetoproteins
Liver Neoplasms
Liver Transplantation
Reference Values

Keywords

  • hepatocellular carcinoma
  • prognostic variable
  • survival benefit
  • treatment selection

ASJC Scopus subject areas

  • Hepatology

Cite this

The concept of therapeutic hierarchy for patients with hepatocellular carcinoma : A multicenter cohort study. / Vitale, Alessandro; Farinati, Fabio; Pawlik, Timothy M.; Frigo, Anna Chiara; Giannini, Edoardo G.; Napoli, Lucia; Ciccarese, Francesco; Rapaccini, Gian Ludovico; Di Marco, Maria; Caturelli, Eugenio; Zoli, Marco; Borzio, Franco; Sacco, Rodolfo; Cabibbo, Giuseppe; Virdone, Roberto; Marra, Fabio; Felder, Martina; Morisco, Filomena; Benvegnù, Luisa; Gasbarrini, Antonio; Svegliati-Baroni, Gianluca; Foschi, Francesco Giuseppe; Missale, Gabriele; Masotto, Alberto; Nardone, Gerardo; Colecchia, Antonio; Bernardi, Mauro; Trevisani, Franco; Cillo, Umberto.

In: Liver International, 01.01.2019.

Research output: Contribution to journalArticle

Vitale, A, Farinati, F, Pawlik, TM, Frigo, AC, Giannini, EG, Napoli, L, Ciccarese, F, Rapaccini, GL, Di Marco, M, Caturelli, E, Zoli, M, Borzio, F, Sacco, R, Cabibbo, G, Virdone, R, Marra, F, Felder, M, Morisco, F, Benvegnù, L, Gasbarrini, A, Svegliati-Baroni, G, Foschi, FG, Missale, G, Masotto, A, Nardone, G, Colecchia, A, Bernardi, M, Trevisani, F & Cillo, U 2019, 'The concept of therapeutic hierarchy for patients with hepatocellular carcinoma: A multicenter cohort study', Liver International. https://doi.org/10.1111/liv.14154
Vitale, Alessandro ; Farinati, Fabio ; Pawlik, Timothy M. ; Frigo, Anna Chiara ; Giannini, Edoardo G. ; Napoli, Lucia ; Ciccarese, Francesco ; Rapaccini, Gian Ludovico ; Di Marco, Maria ; Caturelli, Eugenio ; Zoli, Marco ; Borzio, Franco ; Sacco, Rodolfo ; Cabibbo, Giuseppe ; Virdone, Roberto ; Marra, Fabio ; Felder, Martina ; Morisco, Filomena ; Benvegnù, Luisa ; Gasbarrini, Antonio ; Svegliati-Baroni, Gianluca ; Foschi, Francesco Giuseppe ; Missale, Gabriele ; Masotto, Alberto ; Nardone, Gerardo ; Colecchia, Antonio ; Bernardi, Mauro ; Trevisani, Franco ; Cillo, Umberto. / The concept of therapeutic hierarchy for patients with hepatocellular carcinoma : A multicenter cohort study. In: Liver International. 2019.
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abstract = "Background: The Italian Liver Cancer (ITA.LI.CA) prognostic system for patients with hepatocellular carcinoma (HCC) has recently been proposed and validated. We sought to explore the relationship among the ITA.LI.CA prognostic variables (ie tumour stage, functional score based on performance status and Child-Pugh score, and alpha-fetoprotein), treatment selection and survival outcome in HCC patients. Patients and Methods: We analysed 4,867 consecutive HCC patients undergoing six main treatment strategies (liver transplantation, LT; liver resection, LR; ablation, ABL; intra-arterial therapy, IAT; Sorafenib, SOR; and best supportive care, BSC) and enrolled during 2002-2015 in a multicenter Italian database. In order to control pretreatment imbalances in observed variables, a machine learning methodology was used and inverse probability of treatment weights (IPTW) was calculated. An IPTW-adjusted multivariate survival model that included ITA.LI.CA prognostic variables, treatment period and treatment strategy was then developed. The survival benefit of HCC treatments was described as a hazard ratio (95{\%} confidence interval), using BSC as a reference value and as predicted median survival. Results: After the IPTW, the six treatment groups became well balanced for most baseline characteristics. In the IPTW-adjusted multivariate survival model, treatment strategy was found to be the strongest survival predictor, irrespective of ITA.LI.CA prognostic variables and treatment period. The survival benefit of different therapies over BSC was: LT = 0.19 (0.18-0.20); RES = 0.40 (0.37-0.42); ABL 0.42 (0.40-0.44); IAT = 0.58 (0.55-0.61); SOR = 0.92 (0.87-0.97). This multivariate model was then used to predict median survival for each therapy within each ITA.LI.CA stage. Conclusion: The concept of therapeutic hierarchy was established within each ITA.LI.CA stage.",
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author = "Alessandro Vitale and Fabio Farinati and Pawlik, {Timothy M.} and Frigo, {Anna Chiara} and Giannini, {Edoardo G.} and Lucia Napoli and Francesco Ciccarese and Rapaccini, {Gian Ludovico} and {Di Marco}, Maria and Eugenio Caturelli and Marco Zoli and Franco Borzio and Rodolfo Sacco and Giuseppe Cabibbo and Roberto Virdone and Fabio Marra and Martina Felder and Filomena Morisco and Luisa Benvegn{\`u} and Antonio Gasbarrini and Gianluca Svegliati-Baroni and Foschi, {Francesco Giuseppe} and Gabriele Missale and Alberto Masotto and Gerardo Nardone and Antonio Colecchia and Mauro Bernardi and Franco Trevisani and Umberto Cillo",
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T1 - The concept of therapeutic hierarchy for patients with hepatocellular carcinoma

T2 - A multicenter cohort study

AU - Vitale, Alessandro

AU - Farinati, Fabio

AU - Pawlik, Timothy M.

AU - Frigo, Anna Chiara

AU - Giannini, Edoardo G.

AU - Napoli, Lucia

AU - Ciccarese, Francesco

AU - Rapaccini, Gian Ludovico

AU - Di Marco, Maria

AU - Caturelli, Eugenio

AU - Zoli, Marco

AU - Borzio, Franco

AU - Sacco, Rodolfo

AU - Cabibbo, Giuseppe

AU - Virdone, Roberto

AU - Marra, Fabio

AU - Felder, Martina

AU - Morisco, Filomena

AU - Benvegnù, Luisa

AU - Gasbarrini, Antonio

AU - Svegliati-Baroni, Gianluca

AU - Foschi, Francesco Giuseppe

AU - Missale, Gabriele

AU - Masotto, Alberto

AU - Nardone, Gerardo

AU - Colecchia, Antonio

AU - Bernardi, Mauro

AU - Trevisani, Franco

AU - Cillo, Umberto

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: The Italian Liver Cancer (ITA.LI.CA) prognostic system for patients with hepatocellular carcinoma (HCC) has recently been proposed and validated. We sought to explore the relationship among the ITA.LI.CA prognostic variables (ie tumour stage, functional score based on performance status and Child-Pugh score, and alpha-fetoprotein), treatment selection and survival outcome in HCC patients. Patients and Methods: We analysed 4,867 consecutive HCC patients undergoing six main treatment strategies (liver transplantation, LT; liver resection, LR; ablation, ABL; intra-arterial therapy, IAT; Sorafenib, SOR; and best supportive care, BSC) and enrolled during 2002-2015 in a multicenter Italian database. In order to control pretreatment imbalances in observed variables, a machine learning methodology was used and inverse probability of treatment weights (IPTW) was calculated. An IPTW-adjusted multivariate survival model that included ITA.LI.CA prognostic variables, treatment period and treatment strategy was then developed. The survival benefit of HCC treatments was described as a hazard ratio (95% confidence interval), using BSC as a reference value and as predicted median survival. Results: After the IPTW, the six treatment groups became well balanced for most baseline characteristics. In the IPTW-adjusted multivariate survival model, treatment strategy was found to be the strongest survival predictor, irrespective of ITA.LI.CA prognostic variables and treatment period. The survival benefit of different therapies over BSC was: LT = 0.19 (0.18-0.20); RES = 0.40 (0.37-0.42); ABL 0.42 (0.40-0.44); IAT = 0.58 (0.55-0.61); SOR = 0.92 (0.87-0.97). This multivariate model was then used to predict median survival for each therapy within each ITA.LI.CA stage. Conclusion: The concept of therapeutic hierarchy was established within each ITA.LI.CA stage.

AB - Background: The Italian Liver Cancer (ITA.LI.CA) prognostic system for patients with hepatocellular carcinoma (HCC) has recently been proposed and validated. We sought to explore the relationship among the ITA.LI.CA prognostic variables (ie tumour stage, functional score based on performance status and Child-Pugh score, and alpha-fetoprotein), treatment selection and survival outcome in HCC patients. Patients and Methods: We analysed 4,867 consecutive HCC patients undergoing six main treatment strategies (liver transplantation, LT; liver resection, LR; ablation, ABL; intra-arterial therapy, IAT; Sorafenib, SOR; and best supportive care, BSC) and enrolled during 2002-2015 in a multicenter Italian database. In order to control pretreatment imbalances in observed variables, a machine learning methodology was used and inverse probability of treatment weights (IPTW) was calculated. An IPTW-adjusted multivariate survival model that included ITA.LI.CA prognostic variables, treatment period and treatment strategy was then developed. The survival benefit of HCC treatments was described as a hazard ratio (95% confidence interval), using BSC as a reference value and as predicted median survival. Results: After the IPTW, the six treatment groups became well balanced for most baseline characteristics. In the IPTW-adjusted multivariate survival model, treatment strategy was found to be the strongest survival predictor, irrespective of ITA.LI.CA prognostic variables and treatment period. The survival benefit of different therapies over BSC was: LT = 0.19 (0.18-0.20); RES = 0.40 (0.37-0.42); ABL 0.42 (0.40-0.44); IAT = 0.58 (0.55-0.61); SOR = 0.92 (0.87-0.97). This multivariate model was then used to predict median survival for each therapy within each ITA.LI.CA stage. Conclusion: The concept of therapeutic hierarchy was established within each ITA.LI.CA stage.

KW - hepatocellular carcinoma

KW - prognostic variable

KW - survival benefit

KW - treatment selection

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