Literature DB >> 27847278

Validation of the Hong Kong Liver Cancer Staging System in Determining Prognosis of the North American Patients Following Intra-arterial Therapy.

Jae Ho Sohn1, Rafael Duran1, Yan Zhao1, Florian Fleckenstein1, Julius Chapiro1, Sonia Sahu1, Rüdiger E Schernthaner1, Tianchen Qian2, Howard Lee1, Li Zhao1, James Hamilton3, Constantine Frangakis2, MingDe Lin4, Riad Salem5, Jean-Francois Geschwind6.   

Abstract

BACKGROUND & AIMS: There is debate over the best way to stage hepatocellular carcinoma (HCC). We attempted to validate the prognostic and clinical utility of the recently developed Hong Kong Liver Cancer (HKLC) staging system, a hepatitis B-based model, and compared data with that from the Barcelona Clinic Liver Cancer (BCLC) staging system in a North American population that underwent intra-arterial therapy (IAT).
METHODS: We performed a retrospective analysis of data from 1009 patients with HCC who underwent IAT from 2000 through 2014. Most patients had hepatitis C or unresectable tumors; all patients underwent IAT, with or without resection, transplantation, and/or systemic chemotherapy. We calculated HCC stage for each patient using 5-stage HKLC (HKLC-5) and 9-stage HKLC (HKLC-9) system classifications, and the BCLC system. Survival information was collected up until the end of 2014 at which point living or unconfirmed patients were censored. We compared performance of the BCLC, HKLC-5, and HKLC-9 systems in predicting patient outcomes using Kaplan-Meier estimates, calibration plots, C statistic, Akaike information criterion, and the likelihood ratio test.
RESULTS: Median overall survival time, calculated from first IAT until date of death or censorship, for the entire cohort (all stages) was 9.8 months. The BCLC and HKLC staging systems predicted patient survival times with significance (P < .001). HKLC-5 and HKLC-9 each demonstrated good calibration. The HKLC-5 system outperformed the BCLC system in predicting patient survival times (HKLC C = 0.71, Akaike information criterion = 6242; BCLC C = 0.64, Akaike information criterion = 6320), reducing error in predicting survival time (HKLC reduced error by 14%, BCLC reduced error by 12%), and homogeneity (HKLC chi-square = 201, P < .001; BCLC chi-square = 119, P < .001) and monotonicity (HKLC linear trend chi-square = 193, P < .001; BCLC linear trend chi-square = 111, P < .001). Small proportions of patients with HCC of stages IV or V, according to the HKLC system, survived for 6 months and 4 months, respectively.
CONCLUSIONS: In a retrospective analysis of patients who underwent IAT for unresectable HCC, we found the HKLC-5 staging system to have the best combination of performances in survival separation, calibration, and discrimination; it consistently outperformed the BCLC system in predicting survival times of patients. The HKLC system identified patients with HCC of stages IV and V who are unlikely to benefit from IAT.
Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Liver Cancer; Predicted Outcome; Prognosis; Risk Factors

Mesh:

Year:  2016        PMID: 27847278      PMCID: PMC5823259          DOI: 10.1016/j.cgh.2016.10.036

Source DB:  PubMed          Journal:  Clin Gastroenterol Hepatol        ISSN: 1542-3565            Impact factor:   11.382


  32 in total

1.  How I do it: a practical database management system to assist clinical research teams with data collection, organization, and reporting.

Authors:  Howard Lee; Julius Chapiro; Rüdiger Schernthaner; Rafael Duran; Zhijun Wang; Boris Gorodetski; Jean-François Geschwind; MingDe Lin
Journal:  Acad Radiol       Date:  2015-01-29       Impact factor: 3.173

Review 2.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

3.  Usefulness of the HKLC vs. the BCLC staging system in a European HCC cohort.

Authors:  Xavier Adhoute; Guillaume Penaranda; Jean-Pierre Bronowicki; Jean-Luc Raoul
Journal:  J Hepatol       Date:  2014-09-04       Impact factor: 25.083

4.  Comparative study of staging systems for hepatocellular carcinoma in 428 patients treated with radioembolization.

Authors:  Khairuddin Memon; Laura M Kulik; Robert J Lewandowski; Edward Wang; Jonathan Wang; Robert K Ryu; Ryan Hickey; Michael Vouche; Talia Baker; Daniel Ganger; Vanessa L Gates; Ali Habib; Mary F Mulcahy; Riad Salem
Journal:  J Vasc Interv Radiol       Date:  2014-03-07       Impact factor: 3.464

5.  Prognostic accuracy of 12 liver staging systems in patients with unresectable hepatocellular carcinoma treated with transarterial chemoembolization.

Authors:  Christos S Georgiades; Eleni Liapi; Constantine Frangakis; Ju-un Park; Hyung Woo Kim; Kelvin Hong; Jean-Francois H Geschwind
Journal:  J Vasc Interv Radiol       Date:  2006-10       Impact factor: 3.464

6.  Prognosis of hepatocellular carcinoma: the BCLC staging classification.

Authors:  J M Llovet; C Brú; J Bruix
Journal:  Semin Liver Dis       Date:  1999       Impact factor: 6.115

7.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

8.  Safety and efficacy of 90Y radiotherapy for hepatocellular carcinoma with and without portal vein thrombosis.

Authors:  Laura M Kulik; Brian I Carr; Mary F Mulcahy; Robert J Lewandowski; Bassel Atassi; Robert K Ryu; Kent T Sato; Al Benson; Albert A Nemcek; Vanessa L Gates; Michael Abecassis; Reed A Omary; Riad Salem
Journal:  Hepatology       Date:  2008-01       Impact factor: 17.425

9.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

Authors:  Gary S Collins; Johannes B Reitsma; Douglas G Altman; Karel G M Moons
Journal:  Ann Intern Med       Date:  2015-05-19       Impact factor: 25.391

10.  Management of hepatocellular carcinoma: an update.

Authors:  Jordi Bruix; Morris Sherman
Journal:  Hepatology       Date:  2011-03       Impact factor: 17.425

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  11 in total

Review 1.  Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Authors:  Bradley Spieler; Carl Sabottke; Ahmed W Moawad; Ahmed M Gabr; Mustafa R Bashir; Richard Kinh Gian Do; Vahid Yaghmai; Radu Rozenberg; Marielia Gerena; Joseph Yacoub; Khaled M Elsayes
Journal:  Abdom Radiol (NY)       Date:  2021-03-31

Review 2.  Therapy of Intermediate-Stage Hepatocellular Carcinoma: Current Evidence and Clinical Practice.

Authors:  Nathan X Chai; Julius Chapiro
Journal:  Semin Intervent Radiol       Date:  2020-12-11       Impact factor: 1.513

3.  The Prognostic Ability of Major Hepatocellular Carcinoma Staging Systems Is Improved by Including a Treatment Variable.

Authors:  Michael C Wallace; Matthew Knuiman; Yi Huang; George Garas; Leon A Adams; Gerry MacQuillan; David B Preen; Gary P Jeffrey
Journal:  Dig Dis Sci       Date:  2018-05-28       Impact factor: 3.199

Review 4.  New concepts in embolotherapy of HCC.

Authors:  F Pesapane; N Nezami; F Patella; J F Geschwind
Journal:  Med Oncol       Date:  2017-03-16       Impact factor: 3.064

Review 5.  Contemporary Algorithm for the Management of Hepatocellular Carcinoma in 2021: The Northwestern Approach.

Authors:  Adam Swersky; Laura Kulik; Aparna Kalyan; Karen Grace; Juan Carlos Caicedo; Robert J Lewandowski; Riad Salem
Journal:  Semin Intervent Radiol       Date:  2021-10-07       Impact factor: 1.780

6.  Survival Predictability Between the American Joint Committee on Cancer 8th Edition Staging System and the Barcelona Clinic Liver Cancer Classification in Patients with Hepatocellular Carcinoma.

Authors:  Li-Ju Chen; Yun-Jau Chang; Yao-Jen Chang
Journal:  Oncologist       Date:  2020-10-03

7.  Validation of prognostic accuracy of MESH, HKLC, and BCLC classifications in a large German cohort of hepatocellular carcinoma patients.

Authors:  Sophia Heinrich; Martin Sprinzl; Irene Schmidtmann; Elena Heil; Sandra Koch; Carolin Czauderna; Bernd Heinrich; Laurence Philippe P Diggs; Marcus-Alexander Wörns; Roman Kloeckner; Peter R Galle; Jens U Marquardt; Arndt Weinmann
Journal:  United European Gastroenterol J       Date:  2020-01-29       Impact factor: 4.623

8.  A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.

Authors:  Ali Morshid; Khaled M Elsayes; Ahmed M Khalaf; Mohab M Elmohr; Justin Yu; Ahmed O Kaseb; Manal Hassan; Armeen Mahvash; Zhihui Wang; John D Hazle; David Fuentes
Journal:  Radiol Artif Intell       Date:  2019-09-25

9.  Hong Kong Consensus Statements for the Management of Unresectable Hepatocellular Carcinoma.

Authors:  Tom Tan-To Cheung; Philip Chong-Hei Kwok; Stephen Chan; Chin-Cheung Cheung; Ann-Shing Lee; Victor Lee; Hoi-Ching Cheng; Nam-Hung Chia; Charing C N Chong; Tak-Wing Lai; Ada L Y Law; Mai-Yee Luk; Chi Chung Tong; Thomas C C Yau
Journal:  Liver Cancer       Date:  2018-01-25       Impact factor: 11.740

Review 10.  A global view of hepatocellular carcinoma: trends, risk, prevention and management.

Authors:  Ju Dong Yang; Pierre Hainaut; Gregory J Gores; Amina Amadou; Amelie Plymoth; Lewis R Roberts
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2019-08-22       Impact factor: 73.082

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