Volume 72, Issue 1 pp. 198-212
Original Article
Open Access

Prediction of Survival Among Patients Receiving Transarterial Chemoembolization for Hepatocellular Carcinoma: A Response-Based Approach

Guohong Han

Guohong Han

Department of Liver Disease and Digestive Interventional Radiology, Xijing Hospital of Digestive Disease, Fourth Military Medical University, Xi’an, China

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Sarah Berhane

Sarah Berhane

Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom

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Hidenori Toyoda

Hidenori Toyoda

Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan

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Dominik Bettinger

Dominik Bettinger

Department of Medicine II, Faculty of Medicine, Medical Center University of Freiburg, University of Freiburg, Freiburg, Germany

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Omar Elshaarawy

Omar Elshaarawy

National Liver Institute, Menoufia University, Shebeen El-Kom, Egypt

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Anthony W. H. Chan

Anthony W. H. Chan

Department of Pathology, Chinese University of Hong Kong, Hong Kong

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Martha Kirstein

Martha Kirstein

Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany

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Cristina Mosconi

Cristina Mosconi

Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine, Alma Mater Studiorum - University of Bologna, Italy University Hospital of Bologna Sant'Orsola-Malpighi Polyclinic, Bologna, Italy

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Florian Hucke

Florian Hucke

Department of Internal Medicine and Gastroenterology, Klinikum Klagenfurt am Wörthersee, Klagenfurt, Austria

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Daniel Palmer

Daniel Palmer

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom

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David J. Pinato

David J. Pinato

Department of Surgery and Cancer, Imperial College London, London, United Kingdom

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Rohini Sharma

Rohini Sharma

Department of Surgery and Cancer, Imperial College London, London, United Kingdom

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Diego Ottaviani

Diego Ottaviani

UCL Cancer Institute, University College London, London, United Kingdom

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Jeong W. Jang

Jeong W. Jang

Department of Internal Medicine, The Catholic University of Korea, Seoul St. Mary’s Hospital, Seoul, Republic of Korea

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Tim A. Labeur

Tim A. Labeur

Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands

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Otto M. van Delden

Otto M. van Delden

Department of Radiology, Amsterdam University Medical Centers, Amsterdam, the Netherlands

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Mario Pirisi

Mario Pirisi

Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

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Nick Stern

Nick Stern

Department of Gastroenterology and Hepatology, Aintree University Hospital, Liverpool, United Kingdom

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Bruno Sangro

Bruno Sangro

Liver Unit, Clínica Universidad de Navarra IDISNA and CIBEREHD, Pamplona, Spain

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Tim Meyer

Tim Meyer

Research Department of Oncology, UCL Cancer Institute, University College London, London, United Kingdom

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Waleed Fateen

Waleed Fateen

National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Hospitals National Health Service Trust and the University of Nottingham, Nottingham, United Kingdom

Nottingham Digestive Diseases Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

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Marta García-Fiñana

Marta García-Fiñana

Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom

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Asmaa Gomaa

Asmaa Gomaa

National Liver Institute, Menoufia University, Shebeen El-Kom, Egypt

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Imam Waked

Imam Waked

National Liver Institute, Menoufia University, Shebeen El-Kom, Egypt

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Eman Rewisha

Eman Rewisha

National Liver Institute, Menoufia University, Shebeen El-Kom, Egypt

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Guru P. Aithal

Guru P. Aithal

National Institute for Health Research Nottingham Biomedical Research Centre, Nottingham University Hospitals National Health Service Trust and the University of Nottingham, Nottingham, United Kingdom

Nottingham Digestive Diseases Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

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Simon Travis

Simon Travis

Department of Radiology, Nottingham University Hospitals National Health Service Trust, Nottingham, United Kingdom

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Masatoshi Kudo

Masatoshi Kudo

Department of Gastroenterology and Hepatology, Kinki University School of Medicine, Osaka-Sayama, Osaka, Japan

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Alessandro Cucchetti

Alessandro Cucchetti

Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

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Markus Peck-Radosavljevic

Markus Peck-Radosavljevic

Department of Internal Medicine and Gastroenterology, Klinikum Klagenfurt am Wörthersee, Klagenfurt, Austria

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R.B. Takkenberg

R.B. Takkenberg

Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, Amsterdam, the Netherlands

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Stephen L. Chan

Stephen L. Chan

Department of Clinical Oncology, Chinese University of Hong Kong, Shatin, Hong Kong

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Arndt Vogel

Arndt Vogel

Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover, Germany

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Philip J. Johnson

Corresponding Author

Philip J. Johnson

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom

Address Correspondence and Reprint Requests to:

Philip J. Johnson, M.D.

Department of Molecular and Clinical Cancer Medicine

University of Liverpool

2nd floor Sherrington Building

Ashton Street, Liverpool L69 3GE, United Kingdom

E-mail: [email protected]

Tel.: +1-0151-795-8410

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First published: 07 November 2019
Citations: 100
Funding: Supported by National Natural Science Foundation of China grants (81172145 and 81420108020) for data collection in Xijing Hospital to G.H.; the National Institute for Health Research grant to D.J.P.; the UK Engineering and Physical Sciences Research Council grant (EP/N014499/1) to S.B. and M.G.F.; and the National Institute for Health Research Nottingham Biomedical Research Centre grant (BRC-1215-20003) to W.F.
Previously presented at the International Liver Cancer Association, London, 2018.
Potential conflict of interest: Dr. Toyoda is on the speakers’ bureau for MSD and AbbVie. Dr. Bettinger received grants from Bayer. Dr. Palmer advises and received grants from Bristol-Myers Squibb, Sirtex, Bayer, Eisai, and AZ. He received grants from BTG. Dr. Pinato consults for ViiV and received grants from Bristol-Myers Squibb and MSD. Dr. van Delden consults for Coch Medical. Dr. Sangro consults for, is on the speakers’ bureau of, and received grants from Bristol-Myers Squibb and Sirtex. He consults and is on the speakers’ bureau for Bayer and AZ. He consults for Adaptimmune, BTG, Lilly, Ipsen, and Enxeo. Dr. Meyer consults for and received grants from Bayer and BTG. He consults for Eisai, Bristol-Myers Squibb, MSD, and Takeda. Dr. Aithal consults for GlaxoSmithKline, Pfizer, and Agios. Dr. Travis advises Guerbet and Boston Scientific. Dr. Kudo advises and received grants from Eisai, Ono, MSD, and Bristol-Myers Squibb. He advises Bayer and Eli Lilly and received grants from AbbVie, Takeda, Gilead, Otsuka, and Taiho. Dr. Takkenberg advises and is on the speakers’ bureau for Norgine. He advises Gilead, is on the speakers’ bureau for Gore and Bayer, and received grants from GastroStart and ZonMw.

Abstract

Background and Aims

The heterogeneity of intermediate-stage hepatocellular carcinoma (HCC) and the widespread use of transarterial chemoembolization (TACE) outside recommended guidelines have encouraged the development of scoring systems that predict patient survival. The aim of this study was to build and validate statistical models that offer individualized patient survival prediction using response to TACE as a variable.

Approach and Results

Clinically relevant baseline parameters were collected for 4,621 patients with HCC treated with TACE at 19 centers in 11 countries. In some of the centers, radiological responses (as assessed by modified Response Evaluation Criteria in Solid Tumors [mRECIST]) were also accrued. The data set was divided into a training set, an internal validation set, and two external validation sets. A pre-TACE model (“Pre-TACE-Predict”) and a post-TACE model (“Post-TACE-Predict”) that included response were built. The performance of the models in predicting overall survival (OS) was compared with existing ones. The median OS was 19.9 months. The factors influencing survival were tumor number and size, alpha-fetoprotein, albumin, bilirubin, vascular invasion, cause, and response as assessed by mRECIST. The proposed models showed superior predictive accuracy compared with existing models (the hepatoma arterial embolization prognostic score and its various modifications) and allowed for patient stratification into four distinct risk categories whose median OS ranged from 7 months to more than 4 years.

Conclusions

A TACE-specific and extensively validated model based on routinely available clinical features and response after first TACE permitted patient-level prognostication.

Abbreviations

  • AFP
  • alpha-fetoprotein
  • ALBI
  • albumin-bilirubin
  • BCLC
  • Barcelona Clinic Liver Cancer
  • CI
  • confidence interval
  • CR
  • complete response
  • DAA
  • direct-acting antiviral
  • DEB
  • drug-eluting bead
  • HAP
  • hepatoma arterial embolization prognostic
  • HBV
  • hepatitis B virus
  • HCC
  • hepatocellular carcinoma
  • HCV
  • hepatitis C virus
  • HR
  • hazard ration
  • KM
  • Kaplan-Meier
  • mHAP-II
  • modified HAP-II
  • mHAP-III
  • modified HAP-III
  • mRECIST
  • modified Response Evaluation Criteria in Solid Tumors
  • NAFLD
  • nonalcoholic fatty liver disease
  • OS
  • overall survival
  • PD
  • progressive disease
  • PR
  • partial response
  • SD
  • stable disease
  • SVR
  • sustained virological response
  • TACE
  • transarterial chemoembolization
  • VI
  • vascular invasion
  • International guidelines recommend transarterial chemoembolization (TACE) for patients with hepatocellular carcinoma (HCC) at the Barcelona Clinic Liver Cancer (BCLC) intermediate stage (B) or for those at the BCLC 0/A stage who are not candidates for percutaneous ablation, liver resection, or transplantation by virtue of the tumor location, portal hypertension, or comorbidity.1, 2 This recommendation was based on two randomized trials and subsequent studies.3-7 However, the heterogeneity of this “intermediate” population has been extensively documented, and the unmet need of stratification according to baseline features has been emphasized.8, 9

    Among those in the cohort who are classified as “ideal candidates” for TACE, an expected median survival in the order of 30 months is quoted, but even within this patient group, there is a wide variation in survival.5, 6, 10 However, in practice, many patients receive TACE outside the guideline criteria. For example, vascular invasion (VI) is not always considered a contraindication to TACE11; therefore, in this expanded population, variation in survival may be even greater. This wide variability in survival has led to attempts to define the prognostic features and combine these into scores (or “models”) that can be applied to assess prognosis at a subgroup or individual patient level. One frequently quoted aim is to identify that subgroup of patients who respond poorly to TACE and may be considered for systemic therapies.8, 12

    Among the first prognostic scores to be developed was the hepatoma arterial embolization prognostic (HAP) score, which is based on a simple points system involving tumor size, alpha-fetoprotein (AFP), bilirubin, and albumin.13 The HAP score (which was enhanced by Kim et al.14 by adding tumor number [referred to as the modified HAP-II {mHAP-II}]) has the advantage of easy applicability and simplicity but does not permit individual patient-level prognostication. This limitation was overcome by Cappelli et al., who developed the modified HAP-III (mHAP-III) to include HAP variables, together with tumor number in their continuous (as opposed to dichotomized) form.15 mHAP-III permits individual patient-level prognostication expressed as the likelihood of survival at a specific period of time after the first TACE.

    A second, and more important, limitation of current scores is that they may be HCC-specific rather than TACE-specific.

    In this study, it was confirmed that the HAP score is HCC-specific rather than TACE-specific, and we present TACE-specific models that permit accurate individualized patient survival prediction.

    Patients and Methods

    This analysis was reported according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.16

    As a prelude to the main study, the specificity of the HAP score for patients undergoing TACE was examined in 3,556 patients with early HCC who underwent resection and in 967 patients with advanced HCC who received sorafenib within clinical trials.17, 18

    In the main study, the reported TACE cohort19 was expanded by collecting further cases in which the response to TACE according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST)20, 21 was recorded. This analysis has involved only patients who were classified by the local investigator as undergoing TACE as their primary and first treatment. Patients whose TACE was used as a bridge to transplantation or other potentially curative treatment options were excluded, as were patients with extrahepatic metastasis. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the appropriate institutional review committee.

    All participating centers had specific expertise in the management of HCC and the practice of TACE. There were 19 centers representing 11 different countries, including a reported multicenter cohort22, 23 that comprised patients from London (United Kingdom), Osaka (Japan), Seoul (Korea), and Novara (Italy) (Tables 1 and 2). Most centers used “conventional” TACE, although several moved to drug-eluting bead (DEB)–based TACE after 2008. In all centers, patients were followed up by computed tomography (CT) or magnetic resonance imaging scans once every 3 months after stable disease (SD) had been attained.

    Table 1. Patient Characteristics
    Variable Xi’an, China (N = 786) Freiburg, Germany (N = 407) Menofia, Egypt (N = 391) Hannover, Germany (N = 356) Hong Kong 1 (N = 140) Hong Kong 2 (N = 242) Bologna, Italy (N = 234) Ogaki, Japan (N = 613) Amsterdam, NL (N = 138) Pamplona, Spain (N = 85) Birmingham, UK (N = 167) Liverpool, UK (N = 132) London, UK 1 (N = 114) London, UK 2 (N = 84) Nottingham, UK (N = 41) Klagenfurt, Austria (N = 220) Multicenter (N = 471)
    Age (years) 54 (11.9), n = 785 67 (9.3), n = 407 59 (8.3), n = 391 64 (11.0), n = 356 64 (10.4), n = 140 62 (11.3), n = 242 65 (9.7), n = 234 65 (9.7), n = 613 68 (9.8), n = 138 64 (10.5), n = 84 64 (10.3), n = 166 69 (9.4), n = 132 64 (10.1), n = 114 65 (9.6), n = 84 70 (8.8), n = 41 67 (9.8), n = 220 69 (10.6), n = 471
    Male, n (%) 654 (83.9), n = 780 349 (85.8), n = 407 282 (72.1), n = 391 286 (80.3), n = 356 121 (86.4), n = 140 209 (86.4), n = 242 177 (75.6), n = 234 456 (74.4), n = 613 106 (76.8), n = 138 72 (84.7), n = 85 133 (79.6), n = 167 112 (84.9), n = 132 99 (86.8), n = 114 73 (86.9), n = 84 33 (80.5), n = 41 189 (85.9), n = 220 348 (73.9), n = 471
    Cause, n (%) n = 786 n = 407 n = 379 n = 354 n = 140 n = 242 n = 233 n = 610 n = 133 n = 81 n = 94 n = 121 n = 106 n = 83 n = 41 n = 205 n = 471
    HCV 19 (2.4) 87 (21.4) 347 (91.6) 82 (23.2) 11 (7.9) 18 (7.4) 129 (55.4) 349 (57.2) 29 (21.8) 42 (51.9) 26 (27.7) 10 (8.3) 27 (25.5) 23 (27.7) 5 (12.2) 63 (30.7) 232 (49.3)
    HBV 708 (90.1) 42 (10.3) 24 (6.3) 56 (15.8) 111 (79.3) 196 (81.0) 27 (11.6) 108 (17.7) 11 (8.3) 9 (11.1) 16 (17.0) 2 (1.7) 17 (16.0) 8 (9.6) 0 (0) 16 (7.8) 98 (20.8)
    Alcohol 1 (0.1) 154 (37.8) 0 (0) 100 (28.3) 0 (0) 0 (0) 27 (11.6) 0 (0) 43 (32.3) 15 (18.5) 42 (44.7) 32 (26.5) 16 (15.1) 10 (12.1) 14 (34.2) 102 (49.8) 85 (18.1)
    ther 58 (7.4) 124 (30.5) 8 (2.1) 116 (32.8) 18 (12.9) 28 (11.6) 50 (21.5) 153 (25.1) 50 (37.6) 15 (18.5) 10 (10.6) 77 (63.6) 46 (43.4) 42 (50.6) 22 (53.7) 24 (11.7) 56 (11.9)
    ECOG 0/1, n (%) n = 786 n = 407 n = 391 N/A N/A n = 125 n = 234 N/A n = 132 n = 85 n = 40 N/A n = 57 n = 74 n = 41 n = 220 N/A
    0 427 (54.3) 311 (76.4) 324 (82.9) N/A N/A 55 (44.0) 192 (82.1) N/A 62 (47.0) 72 (84.7) 26 (65.0) N/A 35 (61.4) 40 (54.1) 24 (58.5) 220 (100) N/A
    1 355 (45.2) 46 (11.3) 67 (17.1) N/A N/A 68 (54.4) 42 (18.0) N/A 54 (40.9) 10 (11.8) 9 (22.5) N/A 13 (22.8) 22 (29.7) 12 (29.3) 0 (0) N/A
    2 4 (0.5) 50 (12.3) 0 (0) N/A N/A 1 (0.8) 0 (0) N/A 15 (11.4) 2 (2.4) 3 (7.5) N/A 9 (15.8) 11 (14.9) 5 (12.2) 0 (0) N/A
    3 0 (0) 0 (0) 0 (0) N/A N/A 1 (0.8) 0 (0) N/A 1 (0.8) 1 (1.2) 2 (5.0) N/A 0 (0) 1 (1.4) 0 (0) 0 (0) N/A
    Baseline Child-Pugh grade, n (%) n = 786 n = 407 n = 391 n = 338 n = 140 n = 242 n = 234 n = 613 n = 134 n = 85 n = 167 n = 132 n = 91 n = 83 n = 40 n = 220 n = 469
    A 712 (90.6) 291 (71.5) 283 (72.4) 230 (68.1) 107 (76.4) 195 (80.6) 156 (66.7) 320 (52.2) 104 (77.6) 51 (60.0) 151 (90.4) 120 (90.9) 68 (74.7) 70 (84.3) 27 (67.5) 136 (61.8) 343 (73.1)
    B 72 (9.2) 104 (25.6) 108 (27.6) 105 (31.1) 31 (22.1) 43 (17.8) 71 (30.3) 255 (41.6) 29 (21.6) 31 (36.5) 16 (9.6) 12 (9.1) 22 (24.2) 13 (15.7) 11 (27.5) 84 (38.2) 124 (26.4)
    C 2 (0.3) 12 (3.0) 0 (0) 3 (0.9) 2 (1.4) 4 (1.7) 7 (3.0) 38 (6.2) 1 (0.8) 3 (3.5) 0 (0) 0 (0) 1 (1.1) 0 (0) 2 (5.0) 0 (0) 2 (0.4)
    Median follow-up, months (95% CI) 45.0 (41.7, 51.2), n = 784 89.2 (68.4, 129.0), n = 406 47.3 (44.7, 50.9), n = 3,420
    Median OS, months (95% CI) 14.6 (13.0, 16.6), n = 784 17.6 (14.8, 20.4), n = 406 21.2 (20.3, 22.2), n = 3,420
    • * Centers involved London (UK), Osaka (Japan), Seoul (Korea), and Novara (Italy).
    • Abbreviations: ECOG, Eastern Cooperative Oncology Group; N/A, not applicable; NL, the Netherlands; UK, United Kingdom.
    Table 2. Tumor Characteristics and Laboratory Results
    Variable Xi’an, China (N = 786) Freiburg, Germany (N = 407) Menofia, Egypt (N = 391) Hannover, Germany (N = 356) Hong Kong 1 (N = 140) Hong Kong 2 (N = 242) Bologna, Italy (N = 234) Ogaki, Japan (N = 613) Amsterdam, NL (N = 138) Pamplona, Spain (N = 85) Birmingham, UK (N = 167) Liverpool, UK (N = 132) London, UK 1 (N = 114) London, UK 2 (N = 84) Nottingham, UK (N = 41) Klagenfurt, Austria (N = 220) Multicenter (N = 471)
    Solitary tumors, n (%) 396 (51.2), n = 774 132 (32.5), n = 406 161 (41.2), n = 391 77 (21.8), n = 353 59 (42.5), n = 139 82 (33.9), n = 242 108 (46.2), n = 234 190 (31.1), n = 612 42 (30.4), n = 138 27 (31.8), n = 85 59 (36.7), n = 161 63 (47.7), n = 132 48 (42.5), n = 113 30 (35.7), n = 84 18 (43.9), n = 41 73 (33.2), n = 220 107 (27.3), n = 392
    Tumor size (cm) 8.5 (5.5, 11.8), n = 741 5.0 (3.2, 7.6), n = 407 4.5 (3.4, 5.9), n = 391 4.8 (3.1, 7.6), n = 329 5.9 (3.8, 10), n = 136 6.3 (4, 10), n = 230 3 (1.9, 4.3), n = 234 3.4 (2.2, 5.1), n = 564 5.0 (3.9, 6.8), n = 137 6 (3.3, 9.0), n = 79 5.1 (4.0, 7.9), n = 154 4.6 (3.3, 6.8), n = 132 5.0 (3.2, 7.3), n = 109 3.8 (2.1, 6.4), n = 84 5.0 (3.5, 10.7), n = 41 4.0 (3.0, 6.3), n = 220 3.5 (2.2, 5.8), n = 471
    VI, n (%) 242 (30.8), n = 786 20 (4.9), n = 407 0 (0), n = 436 42 (11.9), n = 352 14 (10.0), n = 140 34 (14.1), n = 242 2 (0.9), n = 234 168 (27.5), n = 612 8 (5.8), n = 138 12 (14.1), n = 85 47 (28.1), n = 167 5 (3.8), n = 131 7 (6.2), n = 113 0 (0) 4 (9.8), n = 41 0 (0) 44 (9.3), n = 471
    Baseline ALBI grade n = 784 n = 407 n = 391 n = 355 n = 140 n = 242 n = 234 n = 612 n = 124 n = 75 n = 167 n = 132 n = 97 n = 82 n = 41 n = 220 n = 389
    1 337 (43.0) 128 (31.5) 89 (22.8) 95 (26.8) 35 (25.0) 94 (38.8) 58 (24.8) 81 (13.2) 66 (53.2) 17 (22.7) 78 (46.7) 58 (43.9) 28 (28.9) 35 (42.7) 5 (12.2) 51 (23.2) 124 (31.9)
    2 434 (55.4) 244 (60.0) 262 (67.0) 230 (64.8) 94 (67.1) 135 (55.8) 158 (67.5) 434 (70.9) 48 (38.7) 46 (61.3) 87 (52.1) 71 (53.8) 60 (61.9) 43 (52.4) 31 (75.6) 150 (68.2) 144 (37.0)
    3 13 (1.7) 35 (8.6) 40 (10.2) 30 (8.5) 11 (7.9) 13 (5.4) 18 (7.7) 97 (15.9) 10 (8.1) 12 (16.0) 1 (1.2) 3 (2.3) 9 (9.3) 4 (4.9) 5 (12.2) 19 (8.6) 121 (31.1)
    Baseline ALBI score −2.50 (0.5), n = 784 −2.26 (0.6), n = 407 −2.15 (0.6), n = 391 −2.21 (0.6), n = 355 −2.22 (0.5), n = 140 −2.35 (0.5), n = 242 −2.21 (0.5), n = 234 −1.97 (0.6), n = 612 −2.46 (0.6), n = 124 −2.07 (0.6), n = 75 −2.48 (0.5), n = 167 −2.52 (0.5), n = 132 −2.24 (0.7), n = 97 −2.42 (0.5), n = 82 −2.01 (0.5), n = 41 −2.19 (0.5), n = 220 −1.98 (−3.08, −1.24), n = 389
    Baseline AFP (ng/mL) 356.2 (14.2, 3650.5), n = 776 46.7 (6.7, 472.2), n = 366 79 (12.1, 49 7), n = 391 44 (7, 391), n = 323 89.5 (9, 1356.5), n = 140 126.5 (16, 2300), n = 242 15 (5, 58), n = 191 43 (12, 410), n = 579 28 (5.5, 305.5), n = 128 8.3 (4, 659.7), n = 81 60 (6, 1287), n = 163 10.5 (3, 157.5), n = 100 87.3 (7.1, 1206), n = 102 73.6 (7.5, 469), n = 79 32.5 (4, 546.5), n = 40 26.6 (6, 290.1), n = 219 31.5 (8, 236), n = 466
    Baseline albumin (g/L) 39 (5.4), n = 784 36 (6.1), n = 407 35 (5.8), n = 391 35 (5.9), n = 355 35 (5.2), n = 140 37 (5.2), n = 242 37 (5.1), n = 234 33 (6.1), n = 612 38 (5.6), n = 127 35 (6.0), n = 76 38 (5.2), n = 167 39 (4.7), n = 132 37 (7.0), n = 106 38 (5.3), n = 83 33 (4.7), n = 41 36 (5.4), n = 220 32.7 (23.4, 44.8), n = 389
    Baseline bilirubin (µmol/L) 16.7 (11.7, 22.6), n = 784 17.1 (12.0, 25.7), n = 407 18.8 (13.7, 25.7), n = 391 15 (10, 24), n = 356 14 (9, 22), n = 140 17 (11, 24), n = 242 21.6 (14.0, 36.9), n = 234 15.4 (11.1, 23.9), n = 612 16 (8, 26), n = 127 27.7 (15. 6, 42.5), n = 84 14 (9, 24), n = 167 14 (9.5, 23), n = 132 20 (14, 32), n = 97 17 (12, 25), n = 82 15 (10, 22), n = 41 21.6 (14.4, 32.3), n = 220 13.7 (10.3, 21), n = 471
    Baseline AST (IU/L) 50 (35, 75.5), n = 784 65 (43, 101), n = 407 65 (46, 93), n = 391 N/A N/A N/A N/A N/A 53 (35, 92), n = 126 N/A 51 (35, 84), n = 167 N/A N/A 68.5 (44, 107.5), n = 80 51.5 (37.5, 76), n = 20 52 (34.5, 80), n = 220 53 (36, 75), n = 449
    Baseline platelets (× 109) 128 (81, 185), n = 786 155 (108, 221), n = 407 N/A N/A 155 (91, 240), n = 138 162 (111, 252), n = 125 N/A 102 (69, 147), n = 500 142 (106, 195), n = 126 110 (76, 165), n = 85 N/A N/A N/A 130 (82, 202), n = 83 154 (110.5, 231.5), n = 40 117 (82, 173.5), n = 220 124 (85, 178), n = 392
    Baseline INR 1.1 (1.0, 1.2), n = 778 1.1 (1.0, 1.2), n = 407 1.2 (1.1, 1.3), n = 391 N/A 1.1 (1.1, 1.2), n = 140 0.9 (0.9, 1.0), n = 242 1.3 (1.1, 1.4), n = 234 N/A 1.1 (1.1, 1.2), n = 122 1.2 (1.0, 1.2), n = 77 1.1 (1.0, 1.2), n = 167 1.1 (1.0, 1.2), n = 132 1.2 (1.1, 1.4), n = 103 1.2 (1.1, 1.3), n = 83 1.0 (0.9, 1.1), n = 41 N/A 1.1 (1.1, 1.2), n = 350
    Baseline creatinine 80 (68, 93), n = 781 79.6 (61.9, 93.7), n = 406 72.5 (61.9, 96.4), n = 391 N/A 83 (72.5, 98.5), n = 140 N/A N/A N/A 76 (64, 91), n = 127 79.6 (70.7, 93.7), n = 82 87 (76, 101), n = 167 84 (73, 98), n = 132 87 (74, 99), n = 106 N/A 73 (61, 82), n = 41 80.4 (68.1, 96.4), n = 220 N/A
    Response after first TACE n = 786 n = 407 n = 390 N/A N/A N/A n = 234 N/A n = 105 N/A N/A N/A N/A N/A n = 39 n = 212 n = 461
    CR 133 (16.9) 6 (1.5) 167 (42.8) N/A N/A N/A 125 (53.4) N/A 18 (17.1) N/A N/A N/A N/A N/A 7 (18.0) 11 (5.2) 158 (34.3)
    PR 203 (25.8) 57 (14.0) 150 (38.5) N/A N/A N/A 96 (41.0) N/A 54 (51.4) N/A N/A N/A N/A N/A 9 (23.1) 68 (32.1) 110 (23.9)
    SD 268 (34.1) 230 (56.5) 49 (12.6) N/A N/A N/A 2 (0.9) N/A 11 (10.5) N/A N/A N/A N/A N/A 10 (25.6) 116 (54.7) 80 (17.4)
    PD 182 (23.2) 114 (28.0) 24 (6.2) N/A N/A N/A 11 (4.7) N/A 22 (21.0) N/A N/A N/A N/A N/A 13 (33.3) 17 (8.0) 113 (24.5)
    • * Centers involved London (UK), Osaka (Japan), Seoul (Korea), and Novara (Italy).
    • Abbreviations: AST, aspartate transaminase; ECOG, Eastern Cooperative Oncology Group; N/A, not applicable; NL, the Netherlands; UK, United Kingdom.

    Baseline variables available in all the centers were age, sex, cause (hepatitis C virus [HCV], hepatitis B virus [HBV], alcohol, or “other”), tumor number (solitary or multiple), tumor size (centimeters), VI, Child-Pugh grade, albumin (grams per liter), bilirubin (micromoles per liter), and AFP (nanograms per milliliter). The approach to TACE (DEB-based or lipiodol-based methods) was not proscribed, although no case received transarterial radioembolization.

    The “other” cause comprised mainly patients with nonalcoholic fatty liver disease (NAFLD), other types of chronic liver disease, and more than one cause. The first TACE procedure was undertaken within 6 weeks of diagnosis, and laboratory data were recorded during that period.

    VI (including portal vein, hepatic vein, and inferior vena cava involvement) was assessed in the portal phase of CT and supplemented where appropriate by arterial portography and classified as “present” or “absent.” Response assessments according to mRECIST20, 21 were made within the 6 to 9 weeks following the first TACE treatment. mRECIST response was categorized as complete response (CR), partial response (PR), SD, and progressive disease (PD). mRECIST data were available in eight of the 17 cohorts (2,688 patients). This analysis did not take into account further TACE treatments undertaken after the first one. Liver function was assessed by the Child-Pugh grade (as graded by the local investigator) and the albumin-bilirubin (ALBI) score, the latter being graded according to the published cut-off points.24 Grades 1, 2, and 3 refer to good, intermediate, and poor liver function, respectively. Data on treatment of hepatitis C with direct-acting antivirals (DAAs) were not collected, but an estimate of the number who might have received this therapy was gained by assessing the date of TACE treatment, assuming there were only a very limited number who would receive DAAs before January 2012.

    After generation of the models, as described below, they were externally validated in independent data sets from China and Germany, representing “Eastern” and “Western” cohorts respectively. External validation and calibration were undertaken using methods described by Royston and Altman.25, 26

    Statistical Methods

    Analysis was carried out using Stata/SE 14.1 (StataCorp, TX). Continuous variables were reported as the mean (with standard deviation) or median (with interquartile range), the latter for variables with skewed distributions. Categorical variables were presented as percentages. Logarithmic transformation (log10) was applied to skewed variables. Overall survival (OS) was calculated from date of treatment to date of death. Patients who were still alive were censored at date of last follow-up. Survival curves were plotted using the Kaplan-Meier (KM) method. For the Post-TACE-Predict model, which considers mRECIST response, OS was calculated from the date of response assessment rather than from the date of treatment. Patients with missing data were excluded.

    All patients, excluding those from the largest Eastern (Xi’an, n = 786) and Western (Freiburg, n = 407) cohorts, were randomly split into two equally sized groups (n = 1,714), one for deriving the model(s) and one for internal validation of the model (Supporting Fig. S1A). Patients were randomly split by generating a pseudorandom number from a uniform distribution (0, 1) for each patient, followed by shuffling patients by sorting these random numbers. Subsequently, the first half of the patients was labeled as the “training set,” and the second half was labeled as the “internal validation set.” External validation was then conducted using Xi’an and Freiburg data sets. Before construction of the models, the applicability of the original HAP and the subsequent mHAP-III models13, 15 was tested on all four subgroups.

    The clustering structure of the data set (i.e., the correlation between observations within a center) was taken into account in the statistical analysis. Robust estimates of the standard errors and variance-covariance matrix were obtained by considering the underlying intracenter correlation (option vce(cluster clustvar) in Stata). Multivariable models were built by backward selection of variables significant at the 10% level. The hazard ratio (HR), 95% confidence interval (CI), and P values were reported. The proportional hazards assumption of the models was tested by examining the plots of scaled Schoenfeld residuals against time for each variable.

    Two multivariable Cox regression models were generated:
    • Pre-TACE-Predict model: comprising variables available at baseline, before treatment.
    • Post-TACE-Predict model: incorporating first mRECIST response in addition to baseline features. Not all the cohorts had the mRECIST response recorded; therefore, a smaller set of patients was used (n = 2,688). This set of patients was divided into four subgroups (training, internal, and two external validation samples), as illustrated in Supporting Fig. S1B.

    The linear predictor was derived using the coefficients of each model. To generate four risk categories, reported cutoffs were applied to the linear predictor of the training set at its sixteenth, fiftieth, and eighty-fourth centiles.25 The same cutoffs were used for subsequent groupings in the other cohorts. KM survival curves according to the risk categories were plotted for each of the training and validation sets. Median OS (with 95% CIs), HR, and P values comparing the HR of the reference group (least risk category) to the others were also reported. Prognostic performance of the models (using the nonstratified linear predictor) was measured by Harrell’s C, Gönen and Heller’s K, and Royston-Sauerbrei’s urn:x-wiley:02709139:media:hep31022:hep31022-math-0001.25, 27, 28

    Models were calibrated by comparing model-predicted versus observed survival curves. Model-predicted mean survival curves were generated by applying fractional polynomial regression to approximate the log baseline cumulative hazard function as a smooth function of time.25 Model-predicted versus KM estimates were then plotted according to each risk category in the derivation and validation sets.

    Results

    Within the substudy, the HAP score could clearly identify four distinct prognostic subgroups, both in patients undergoing resection and in those receiving sorafenib for advanced HCC (Supporting Fig. S2A,B). The median OS according to each HAP score and the HR and P values are shown in Supporting Table S1.

    The baseline demographics of the patients from each center are shown in Tables 1 and 2. The percentage of patients who had undergone TACE treatments before January 1, 2012, and January 1, 2013, was 68% and 75.5%, respectively. The percentage of patients with missing data in at least one of the model variables was 14% (training set). For each variable individually, the percentage of missing data was ≤5%.

    mRECIST assessments were undertaken within 9 weeks after first TACE for the majority of patients (94.6%) with a mean (standard deviation) of 5.5 weeks (6.8).

    The overall median survival for the entire group of patients who underwent TACE was 19.9 months (95% CI: 19.1, 20.7), ranging from 13.7 (95% CI: 9.4, 16.9) to 33.8 (95% CI: 27.4, 39.0). Of all the patients, 2.2% (98/4,486) had more than one cause recorded.

    Application of the HAP and mHAP-III Scores

    The HAP score and the mHAP-III score were applied to the present data set. The latter score does not categorize patients into risk categories but provides individual-level prognostication, and this will be compared with HAP later (see the Model Comparisons section). The HAP score stratified the patients into four risk categories in all four subgroups (Supporting Fig. S3A-D). The median OS according to each HAP score as well as the HR and P values are shown in Supporting Table S1.

    Univariable Cox Regressions

    The results from the univariable Cox regression analysis based on the training set are shown in Supporting Table S2. Sex, cause, tumor number, tumor size, VI, AFP, and bilirubin were found to be statistically significant prognostic variables. When survival was assessed from date of response assessment (instead of date of treatment), mRECIST response (following first TACE), cause, tumor number, tumor size, VI, AFP, and bilirubin significantly influenced prognosis.

    Multivariable Cox Regressions

    Pre-TACE-Predict

    The model confirmed the prognostic influence of the variables in the mHAP-III model, namely tumor number, tumor size, AFP, albumin, and bilirubin, in addition to VI and cause (Table 3). It produced four distinct risk categories in each of the four subgroups (Fig. 1A-D). There was no statistically significant difference between the two lowest risk categories in the external validation sets, probably attributable to the low patient numbers in risk category 1 (n = 40-44) (Table 4). Median OS ranged from 35 to 47 months in risk category 1 to 8 to 9 months in risk category 4 (Table 4). The formula used to generate the curves in Fig. 1 was as follows:
    urn:x-wiley:02709139:media:hep31022:hep31022-math-0002()
    Table 3. Multivariable Cox Regression Model
    Variables Pre-TACE-Predict Model Post-TACE-Predict Model
    HR (95% CI) P Value HR (95% CI) P Value
    Tumor number
    Solitary 1 1
    Multiple 1.367 (1.146, 1.630) 0.001 1.229 (1.043, 1.450) 0.014
    log10 Tumor size (cm) 3.497 (2.678, 4.567) <0.0001 3.091 (1.689, 5.659) <0.0001
    Baseline log10 AFP (ng/mL) 1.258 (1.208, 1.311) <0.0001 1.159 (1.065, 1.261) 0.001
    Baseline albumin (g/L) 0.983 (0.966, 0.999) 0.042 N/A N/A
    Baseline log10 bilirubin (µmol/L) 1.581 (1.139, 2.194) 0.006 2.118 (1.466, 3.060) <0.0001
    VI
    No 1 1
    Yes 1.549 (1.185, 2.025) 0.001 1.563 (1.004, 2.433) 0.048
    Cause
    HCV 1 N/A N/A
    HBV 1.160 (1.030, 1.307) 0.015 N/A N/A
    Alcohol 1.395 (1.049, 1.854) 0.022 N/A N/A
    Other 1.235 (1.017, 1.499) 0.033 N/A N/A
    First mRECIST response
    CR N/A N/A 1
    PR N/A N/A 1.598 (1.066, 2.396) 0.023
    SD N/A N/A 3.138 (2.126, 4.630) <0.0001
    PD N/A N/A 3.871 (2.553, 5.871) <0.0001
    Details are in the caption following the image
    Survival according to risk categories as defined by the Pre-TACE-Predict model. KM survival curves in the (A) derivation, (B) internal validation, (C) Eastern external validation, and (D) Western external validation sets. Abbreviation: cat., category.
    Table 4. Median OS (Months) According to the Risk Categories
    Figure Risk Stratification Risk Category N Median OS (95% CI) Hazard Ratio (95% CI) P Value
    1A Derivation set Pre-TACE-Predict model 1 233 41.02 (36.84, 49.24) 1
    2 496 29.18 (27.20, 33.49) 1.57 (1.27, 1.95) <0.0001
    3 495 17.99 (16.81, 19.93) 2.59 (2.10, 3.20) <0.0001
    4 231 8.36 (6.84, 9.77) 5.44 (4.31, 6.86) <0.0001
    1B Internal validation set Pre-TACE-Predict model 1 255 39.18 (34.44, 51.77) 1
    2 483 25.89 (23.09, 27.89) 1.58 (1.29, 1.93) <0.0001
    3 499 18.22 (15.99, 20.23) 2.26 (1.86, 2.75) <0.0001
    4 219 8.65 (7.73, 9.97) 3.93 (3.15, 4.90) <0.0001
    1C External validation set (Eastern) Pre-TACE-Predict model 1 44 46.68 (29.05, 54.05) 1
    2 124 33.82 (28.68, 42.66) 1.36 (0.85, 2.19) 0.201
    3 228 16.88 (14.11, 19.34) 2.66 (1.71, 4.15) <0.0001
    4 330 7.93 (6.94, 9.08) 4.94 (3.19, 7.65) <0.0001
    1D External validation set (Western) Pre-TACE-Predict model 1 40 34.77 (26.81, 47.24) 1
    2 96 23.95 (19.64, 30.69) 1.33 (0.89, 1.98) 0.165
    3 155 17.11 (12.63, 22.50) 1.74 (1.19, 2.53) 0.004
    4 73 8.29 (6.28, 12.27) 2.99 (1.97, 4.53) 0.0001
    2 All patients mRECIST CR 625 42.83 (38.83, 46.68) 1
    PR 745 22.70 (21.09, 24.21) 1.99 (1.71, 2.31) <0.0001
    SD 765 14.28 (13.03, 15.76) 2.95 (2.56, 3.40) <0.0001
    PD 496 8.85 (7.87, 10.13) 4.51 (3.87, 5.26) <0.0001
    3A Derivation set Post-TACE-Predict model 1 101 55.53 (47.53, NR) 1
    2 218 30.26 (26.05, 34.61) 2.50 (1.68, 3.72) <0.0001
    3 214 17.93 (15.26, 20.46) 5.03 (3.40, 7.42) <0.0001
    4 92 8.36 (6.88, 9.34) 12.35 (8.06, 18.93) <0.0001
    3B Internal validation set Post-TACE-Predict model 1 106 51.18 (37.37, 78.22) 1
    2 221 27.50 (24.97, 35.76) 2.14 (1.48, 3.08) <0.0001
    3 220 19.47 (16.51, 24.21) 3.37 (2.36, 4.80) <0.0001
    4 79 8.09 (5.72, 10.53) 7.55 (5.01, 11.39) <0.0001
    3C External validation set (Eastern) Post-TACE-Predict model 1 38 49.80 (28.06, 70.03) 1
    2 99 31.22 (27.53, 37.53) 1.72 (1.02, 2.90) 0.043
    3 203 21.18 (17.60, 24.97) 2.39 (1.46, 3.92) 0.001
    4 375 7.01 (6.09, 7.80) 5.94 (3.68, 9.59) <0.0001
    3D External validation set (Western) Post-TACE-Predict model 1 9 25.13 (11.68, NR) 1
    2 41 34.31 (23.39, 47.11) 1.44 (0.57, 3.67) 0.444
    3 147 22.96 (18.78, 27.34) 1.81 (0.74, 4.44) 0.192
    4 144 9.84 (6.35, 11.78) 3.50 (1.43, 8.56) 0.006

    where HCV is the reference group for cause.

    To generate the four risk categories, the following cutoffs were applied: ≤0.94 (risk category 1), >0.94 to ≤1.47 (risk category 2), >1.47 to ≤2.10 (risk category 3), and >2.10 (risk category 4).

    To calculate the probability of survival at t months for a given patient, the following equation was used:
    urn:x-wiley:02709139:media:hep31022:hep31022-math-0003()

    where S0(t) is 0.89, 0.74, 0.48, and 0.32 for probability at 6, 12, 24, and 36 months, respectively.

    Post-TACE-Predict Model

    Response, as assessed by mRECIST, clearly impacted median survival, which ranged from 42.83 months (95% CI: 38.83, 46.68) in those achieving CR to 8.85 months (95% CI: 7.87, 10.13) in those with PD (Fig. 2), although these figures should be treated with caution because the different response cohorts had different baseline features that would also influence survival. Nonetheless, in the Post-TACE-Predict model, response was clearly an independent prognostic factor (Table 3), in addition to tumor number, tumor size, AFP, bilirubin, and VI.

    Details are in the caption following the image
    KM survival curves according to mRECIST response.
    Four distinct risk categories were observed in each of the four subgroups (Fig. 3A-D); however, there was some overlap between the two lowest risk categories in the Western external validation set, in which the patient numbers were again very low, with only 9 patients in risk category 1. The median OS of the risk categories ranged from 25 to 56 months in risk category 1 to 7 to 10 in risk category 4 (Table 4). The formula to generate the curves in Fig. 3 was as follows:
    urn:x-wiley:02709139:media:hep31022:hep31022-math-0004()
    Details are in the caption following the image
    Survival according to risk categories as defined by the Post-TACE-Predict model. KM survival curves in the (A) derivation, (B) internal validation, (C) Eastern external validation, and (D) Western external validation sets. Abbreviation: cat., category.

    where CR is the reference group for mRECIST.

    To generate the four risk categories, the following cutoffs were applied (as determined by the sixteenth, fiftieth, and eighty-fourth centiles): ≤1.82 (risk category 1), >1.82 to ≤2.49 (risk category 2), >2.49 to ≤3.37 (risk category 3), and >3.37 (risk category 4).

    To calculate the probability of survival at t months for a given patient, the following equation was used:
    urn:x-wiley:02709139:media:hep31022:hep31022-math-0005()

    where S0(t) is 0.92, 0.79, 0.52, and 0.36 for probability at 6, 12, 24, and 36 months, respectively.

    For routine clinical application, a simple online calculator (based on 1-4) that takes the variables from the model(s) and returns the scores, the risk category, and survival likelihood at six monthly intervals between 6 and 36 months after TACE for the individual patient was developed and is available at https://jscalc.io/calc/2omTfeWrmOLc41ei.

    Model Calibration

    Plots of KM estimates versus pre-TACE-predicted and post-TACE-predicted survival curves were, overall, very similar (Supporting Figs. S4 and S5A-D), although it should be noted that there was an overlap in the CIs for the KM estimates in the lowest two risk categories of the external validation sets. This was reflected by the non–statistically significant HRs, as stated above; low patient numbers may have contributed to this.

    Model Comparisons

    Table 5 summarizes the comparisons between the different models by Harrell’s C, Gönen and Heller’s K, and Royston-Sauerbrei’s urn:x-wiley:02709139:media:hep31022:hep31022-math-0006. It confirms that mHAP-III performs better than the HAP score. It also shows a trend of increasingly better survival prediction performance from mHAP-III to the pre-TACE and then post-TACE models.

    Table 5. Model Performance
    Goodness of Fit Test Data Set HAP (SE) mHAP-III (SE) Pre-TACE-Predict Model (SE) Post-TACE-Predict Model (SE)
    Harrell’s C index Training 0.616 (0.010) 0.651 (0.011) 0.682 (0.010) 0.723 (0.013)
    Internal validation 0.624 (0.009) 0.649 (0.010) 0.659 (0.010) 0.693 (0.016)
    External validation (Eastern) 0.640 (0.012) 0.687 (0.012) 0.707 (0.012) 0.730 (0.011)
    External validation (Western) 0.597 (0.015) 0.618 (0.016) 0.613 (0.017) 0.631 (0.017)
    Gönen & Heller’s K Training 0.592 (0.010) 0.633 (0.010) 0.651 (0.010) 0.680 (0.012)
    Internal validation 0.598 (0.010) 0.617 (0.010) 0.623 (0.010) 0.654 (0.013)
    External validation (Eastern) 0.605 (0.013) 0.655 (0.011) 0.667 (0.012) 0.681 (0.012)
    External validation (Western) 0.581 (0.014) 0.545 (0.023) 0.587 (0.016) 0.596 (0.016)
    Royston-Sauerbrei’s urn:x-wiley:02709139:media:hep31022:hep31022-math-0007 Training 0.078 (0.015) 0.132 (0.021) 0.181 (0.020) 0.262 (0.034)
    Internal validation 0.087 (0.016) 0.111 (0.020) 0.120 (0.020) 0.185 (0.030)
    External validation (Eastern) 0.096 (0.023) 0.184 (0.024) 0.209 (0.028) 0.243 (0.034)
    External validation (Western) 0.059 (0.023) 0.050 (0.019) 0.058 (0.022) 0.073 (0.026)
    • SEs were estimated from 200 bootstrap samples.
    • Abbreviation: SE, standard error.

    Discussion

    These models, based on TACE response, stratify survival better than the currently available HAP and mHAP-III models. The median OS was 19.9 months, almost identical to the figures of 19.4 months reported by Lencioni in a large systematic review of published trials involving TACE between 1980 and 2013.29 This suggests that this cohort is representative of the current international practice of TACE for HCC. Furthermore, the clear demonstration that the degree of response has a major and independent impact on survival strongly supports the contention that TACE is indeed altering the natural history.29

    The heterogeneity of intermediate-stage HCC and the widespread use of TACE outside recommended guidelines has encouraged the development of scores that can predict survival after TACE using baseline clinical features.10, 12, 14, 30-32 The first of these, the HAP score, has been internationally validated and enhanced by the addition of a fifth variable, namely tumor number.13, 23, 33 Recognizing the limitations of points-based scores, Cappelli et al. built a model (known as mHAP-III) based on the mHAP-II score but using the same variables in their continuous form, which permitted individual patient prognostication.15 Sposito et al. subsequently validated the mHAP-III model in an independent data set of 298 patients and confirmed its superiority to both HAP and mHAP-II.34 The reported STATE and START scores8 also appear to be valuable in identifying patients as poor or good candidates for TACE but require variables such as C-reactive protein, which were not routinely measured in the centers involved in the present study. Similarly, the ABCR score35 that combines four variables (AFP, BCLC stage, change in Child-Pugh score, and tumor response) aims to identify those with poor prognosis who may not achieve benefit from further TACE. Again, the variables were not available to make a direct comparison (particularly the actual CP scores), but in the follow-up prospective study, an attempt will be made to collect the requisite variables to permit comparison of STATE, START, and ABCR with the current models. It will also be possible to investigate other and potentially valuable additional variables, such as performance status and presence or absence of cirrhosis. Nonetheless, the additional significant variables, the individual patient prognostication, and the extensive international validation are likely to represent a real improvement on existing scores.

    The online calculator (TACE-Predict) provides a simple utility for individual patient-level prognostication. It also permits easy graphical assessment of the importance of the various prognostic variables on ultimate survival. The model involves readily available, routinely recorded clinical variables. The clear correlation of survival with degree of response (as assessed by mRECIST) is consistent with past findings.36 Using these calculators, clinicians will be able to predict the probability of survival at the individual patient level, thereby furthering the ultimate aim of matching “personalized prognosis” to “personalized therapy.” For example, either before proposed first TACE or at the time of first response assessment, the clinician will be able to consider if the predicted survival is appropriate in the light of the potential side effects and toxicities of TACE. This may be particularly clinically valuable in the situation where the predicted outcome is poor, and consideration might be given to systemic therapy. Moreover, all the models were validated on large cohorts of patients to demonstrate the applicability of this approach to both the Eastern and Western practice.

    It is acknowledged that the TACE procedure is unlikely to be entirely consistent across centers. However, this limitation applies equally to all TACE studies, including those on which current guidelines are based. Similarly, there must be interobserver variation in mRECIST classification. Although such variation may be overcome in the clinical trial setting by centralized review of relevant scans, this cannot be a solution in clinical practice. Hence, we made the pragmatic decision that mRECIST classification, as assessed by the local investigator, would be used in the present study.

    Nonetheless, there is considerable heterogeneity in achievement, for example, of CR. The most likely explanation is that those centers with the highest CR (Italy and Egypt) had smaller tumors, more early-stage disease, less VI, and more solitary nodules. The very clear separation of survival according to mRECIST (Fig. 2) suggests that a valid parameter is indeed being measured. It is recognized that calculating OS from mRECIST assessment introduces a degree of variability into the post-TACE model because of the differing times of imaging between patients. This source of variability is, however, intrinsic to the time at which mRECIST is assessed, which is patient-specific, and would affect any model that includes mRECIST, regardless of whether OS is calculated in the model from date of mRECIST response or date of treatment.

    The inherent limitations of a retrospective study are also acknowledged. First, there are several other baseline features that are likely to impact OS and could be included in the analysis, specifically, the extent of VI11 (as opposed to a simple binary classification of present or absent), the structure of the tumor (pseudocapsule versus infiltrative), or liver function kinetics. However, such parameters are not routinely collected, and their inclusion in the study would have limited the applicability of the models. Second, only the first TACE in this study was considered. Assessment of the response after the second TACE or using the “best response” are also options, but both would limit the applicability of the model. Furthermore, patients were excluded who had received TACE as a “bridge to transplantation.” An alternative approach would have been to recruit such patients and censor at the time of transplantation, but, given the usually short period of time between TACE and transplantation, this alternative approach would only have minimal impact on the models. In the prospective study, the investigation of the impact of all the above limitations will be feasible.

    As in many areas of hepatology, the recent availability of curative therapies for HCV will have a broad impact on predictive and therapeutic studies. At present, it is not known whether patients who have developed HCC after a DAA-induced sustained virological response should be classified as HCV-positive in the models, but the number of such cases is likely to be relatively small. The great majority of patients in the present study were recruited before DAAs became widely available. The question of how to assign cause as a variable remains challenging, even in a prospective study. Although cause was shown to be an important prognostic factor, with patients who were HCV-positive surviving longer, several of the cases had multiple causes; however, even with a large data set of more than 4,000 cases, the numbers in individual subgroups, such as those with HCV and alcohol excess or both HBV and HCV, remain too small for meaningful statistical analysis. NAFLD is an increasingly important causal factor in HCC development; however, there are no internationally agreed-on criteria for diagnosis of NAFLD in the setting of HCC. Furthermore, it is acknowledged that the diagnosis of NAFLD is difficult in the setting of cirrhosis (which is the case in most HCCs) because the characteristic features of NAFLD have often “burned out” and are unrecognizable by the time consequential cirrhosis has developed. For all these reasons, it is concluded that the fairest statement of cause is, as used here, simply HBV or HCV or “other.”

    Many programs offer TACE with DEB-TACE as opposed to conventional TACE. This has the advantage of offering a better pharmacokinetic profile by means of sustained and controlled drug release.37 Published meta-analyses, however, suggest that there is little difference in terms of impact on outcome,38-42 albeit with a decreased need for repeat sessions.43 This was therefore not included in the analysis.

    International guidance and expert reviews quote overall post-TACE survival of more than 30 months.1 If the analysis of the data set is confined to those that strictly align with TACE guidelines, survival is indeed in the order of 30 months, and in the model, just using baseline features, some subgroups surviving more than 40 months are identified. The overall median survival of 19.9 months is also similar to that reported in a recent review,29 suggesting that TACE is often prescribed for patients beyond BCLC B. The model and online calculator can help rationalize the use of TACE and avoid interventions with an expected poor prognosis and the associated risks.

    In summary, an extensively validated and TACE-specific model based on routinely available clinical features and response after first TACE is presented. The model and its associated online calculator permit patient-level prognostication and may help clinicians rationalize the use of TACE by avoiding intervention in patients with a predicted poor prognosis.

    Acknowledgment

    We thank Philip J. Johnson and Sarah Berhane for concept and design; Martha Kirstein, Florian Hucke, Cristina Mosconi, David Pinato, Omar Elshaarawy, Tim A. Labeur, Dominik Bettinger, Waleed Fateen, and Bruno Sangro for data collection; Sarah Berhane and Marta García-Fiñana for statistical analysis; and all the authors for writing the article.

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