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Staging and prognostic systems: beyond BCLC?
Alessandro Vitale, MD, PhD, FEBS
U.O.C. di Chirurgia Epatobiliare e dei Trapianti Epatici,
Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua; Italy
ALESSANDRO VITALE, MD, PhDAzienda Ospedaliera e Università di Padova
Il sottoscritto dichiara di non aver avuto negli ultimi 12 mesi conflitto d’interesse in relazione a questa presentazione
e
che la presentazione non contiene discussionedi farmaci in studio o ad uso off-label
(1) Prognosis for individual patients.
(2) Common scale for treatment selection for individualpatients. Avoiding under and overtreatment.
(3) Common scale for the comparison of outcomes amongtreatment methods and institutions and for RCT design.
(4) A graph contrasting outcomes of transplantation to long-term outcomes of preexisting treatment methods for decidingindication of liver transplantation (Transplant benefit).
Kudo M, et al. Dig Dis 2011;29:339–364
Importance of HCC prognostic systems
HCC Prognostic Systems
1. PROGNOSTIC SCORES are “conventional ” prognosticscores that incorporate variables that were significant inmultivariable (Cox, parametric models) survival analyses. The prognostic “weights” of the variables are used to construct the score (DATA BASED).
2. STAGING SYSTEMS typically based on systematic reviewsof the literature and/or expert opinions (EVIDENCE BASED). These systems stratify the HCC population in evolutionary stages exclusively or mainly defined by tumorcharacteristics.
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Staging and prognostic systems: beyond BCLC?
• Data based prognostic scores
• Evidence based staging systems
• Combined prognostic systems
• The issue of treatment allocation
Data based Prognostic Scores
Liu PH, et al. J Hepatol 2016; 64: 601
OKUDA
FRENCH
CLIP TOKYO
Data based Prognostic Scores
Faria SC, et al. Abdominal Imaging 2014
Yang JD, et al. HEPATOLOGY 2012;56:614-621
Data based Prognostic Scores
Model to Estimate Survival In Ambulatory HCC patients (MESIAH)
Chan WHA, et al. Liver Int 2016. In press
Johnson PJ, et al. JCO 2015; 33: 550
Data based Prognostic Scores
PRO: objective and reproducible variables and rigorousstatistical methodology. Accurate survival prediction
CONS: often they are not suitably generalizable to populations different from the one that generated the score, and they don’ t define tumor stages for treatment selection
Data based Prognostic Scores
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Prognostic score(multivariate analysis)
Staging system(literature based)
Data based Prognostic Scores
Staging and prognostic systems: beyond BCLC?
• Data based prognostic scores
• Evidence based staging systems
• Combined prognostic systems
• The issue of treatment allocation
Minagawa M, et al. Ann Surg 2007; 245: 909
T definition originally refersonly to HCC pathologicalcharacteristics of patientsreceiving liver resection
There are 3 TNM surgical stagingsystems:
1) Liver Cancer Study Group of Japan
(LCSGJ)2) American Joint Committee on Cancer(AJCC) and International Union against
Cancer(UICC)3) United Network for Organ Sharing
(UNOS)
Evidence based staging systems
AJCC-UICC TNM 5th edition, 1997AJCC-UICC TNM 6th edition, 2002
UNOS TNM, 2002
Evidence based staging systems
The Barcelona Clinic Liver Cancer(BCLC) Staging Classification for HCC
BCLC stageTumor volume,
number and invasivenessPerformance
status Child-Pugh
0 Very earlySingle < 2 cm
Carcinoma in situ0 A
A Early Single or 3 nodules < 3 cm 0 A – B
B Intermediate Multinodular 0 A – B
C Advanced Portal invasion N1M1 1 – 2 A – B
D Terminal Any of above > 2 C
Cillo U, Vitale A, et al. J Hepatol 2004Cillo U, Vitale A, et al. J Hepatol 2006
Evidence based staging systems
PRO: They are useful to link stages to guidelines for the management of patients with HCC and the design of clinical trials.
CONS: However, these systems are not based on a strong statistical methodology (variables are not weightened) and they often lack prognostic power
Evidence based staging systems
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Liu PH, et al. J Hepatol 2016; 64: 601
Evidence based staging systems
Hsu CY, et al. Hepatology 2013
ECOG PST 1 classified as BCLC B (in original BCLC stage C)
Prognostic pitfalls of BCLC classification
Hsu CY, et al. Liver Int 2016. In press
Prognostic pitfalls of BCLC classification
Prognostic score(multivariate analysis)
Staging system(literature based)
Evidence based staging systems
Staging and prognostic systems: beyond BCLC?
• Data based prognostic scores
• Evidence based staging systems
• Combined prognostic systems
• The issue of treatment allocation
Prognosis Treatment
CombinedPrognostic
SystemPrognostic score(multivariate analysis)
Staging system(literature based)
Combined prognostic systems
• Japan TNM poorperformance in western patients
• No PST• No AFP
Combined prognostic systems
Kudo M, et al. Dig Dis 2011;29:339–364
From 2003
� Kaplan-Meier, log-rank test
� Multivariate log-logistic parametric survival model: ITA.LI.CA staging construction
� Multivariate log-logistic parametric survival model: comparison between systems
� Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)
� 5183 HCC patients (database ITA.LI.CA. - Italian Liver Cancer)
� Training cohort (3628 pts) / Internal validation cohort (1555 pts)
� External validation cohort: 2651 pts (database from Taipei –Taiwan, 2000-2012)
M.F = 3:1
Age (median): 68 yrs
Child (median): 6
MELD (median): 11
HCV+ 60%, HBV+ 17%, Alcool 26%
Diameter max (median): 3 cm
Multifocal: 22%
Metastases: 3%
BCLC: 0 7%, A 33%, B 12%, C 42%, D 6%
Main Treatment:
Resection 11% , Transplant 2%,
Ablation 30% ,TACE 26%,
Sorafenib 3%,
Other 8%, BSC 20%
Staging systems did not respect Proportional Hazard Assumption
• Tumor stage classification based on the literature• Final score based on multivariate analysis
Combined prognostic systems
Hong Kong Liver Cancer Staging System BCLC B HCC: Proposal for a Subclassification
Variables 0 A B1 B2 B3 C
Diameter(cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any
N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any
Vascular invasion
or metastases
no no no no no no no no Intra Exta
UNOS TNM, 2002
ITA.LI.CA TUMOR STAGING
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Combined prognostic systems
Combined prognostic systems
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Combined prognostic systems
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
Staging and prognostic systems: beyond BCLC?
• Data based prognostic scores
• Evidence based staging systems
• Combined prognostic systems
• The issue of treatment allocation
1. Llovet JM, et al. Lancet 2003;362:1907–1917. 2. Marrero JA, et al. Clin Liver Dis 2006;10:339–351. 3. Marrero JA, et al. Hepatology 2005;41:707–716. 4. Llovet JM, et al. Semin Liver Dis 1999;19:329–338.
5. Leung T, et al. Cancer 2002;94:1760–1769. 6. Chevret S, et al. J Hepatol 1999;31:133–141. 7. Schafer DF, et al. Lancet 1999;353:1253–1257. 8. CLIP. Hepatology 1998;28:751–755.
9. Makuuchi M, et al. World J Gastroenterol 2006;12:828–829.
� Prognosis of HCC1
� Most patients have underlyingliver disease
� Key prognostic indicatorsare not clearly defined
� Prognostic indicators vary during the course of disease
� Factors affecting HCC prognosis2,3
� Tumour stage� Liver function� Health status
Patient
TumourLiver
ECOGPS
Child-Pugh
TNM
BCLC4
Okuda 7
CLIP8
JIS9
CUPI5
GRETCH6
HCC Prognostic Factors
The issue of treatment allocation
• … both the stage and the various types of intervention shouldideally be built into the prognostic system..
• There are four main factors affecting prognosis: (a) the stage, aggressiveness and growth rate of the tumor; (b) the general health of the patient; (c) the liver function of the patient; and (d) the specific intervention
• The .. optimal solution would be to develop a prognostic model for each relevant evolutionary stage of the disease (early, intermediate- advanced and terminal) and model into eachstage the variables related to each specific intervention.
Bruix J and Sherman M, et al. Hepatology 2005
The issue of treatment allocation
Treatment selectionPre-determined
Staging System
The issue of treatment allocation
Treatment selectionStaging System
PROBLEM 1: Tumor, liver function, patient related vari ablesdifferently influence Treatment selection and patient p rognosis
PROBLEM 1: PROGNOSTIC PROBLEM
Bruix J and Sherman M. Gastroenterology 2016
?
?
? ? ?
?
The issue of treatment allocation
PROBLEM 1: PROGNOSTIC PROBLEM
Combined prognostic systems
Farinati F, et al. PLoS Med 2016; 13(4):e1002006
PROBLEM 1: PROGNOSTIC PROBLEM
Yau T, et al. Gastroenterology 2014
13 pointsscore??
The issue of treatment allocation
PROBLEM 1: PROGNOSTIC PROBLEM
Yau T, et al. Gastroenterology 2014
The issue of treatment allocation
PROBLEM 1: PROGNOSTIC PROBLEM
Treatment selectionPre-determined
Staging System
The issue of treatment allocation
Treatment selectionStaging System
PROBLEM 2: Treatment selection criteria change with ti me and should be inclusive (indications better than algorithms )
PROBLEM 2: THERAPEUTIC PROBLEM
“The tempting simplicity of the BCLC classification came at a price of low clinical utility by compromising the importance of liver transplantation and locoregional therapies in medicalmanagement of HCC.”
“Designed using data mostly acquired in small Western patientpopulations, the BCLC classification lacks universal applicability in terms of discriminatory ability and prognostic accuracy with regard to treatment recommendations. In fact, the BCLC system precludespatients with more advanced disease from receiving radical therapiesout of safety considerations.”
Chapiro J, et al. Nat Rev Gastroenterol Hepatol 2014; 1 1: 334
The issue of treatment allocation
PROBLEM 2: THERAPEUTIC PROBLEM
Bruix J and Sherman M. Gastroenterology 2016
? ?
?
? ? ?
?
The issue of treatment allocation
PROBLEM 2: THERAPEUTIC PROBLEM
Roayaie S, et al. Hepatology 2015; 62: 440
The issue of treatment allocation
PROBLEM 2: THERAPEUTIC PROBLEM
1302 BCLC A patients undergoing resection
Roayaie S, et al. Hepatology 2015; 62: 440
The issue of treatment allocation
PROBLEM 2: THERAPEUTIC PROBLEM
Kim KM, et al. Liver Int 2016.
A total of 3515 treatment-naıve, newly diagnosed HCC patients at a
single centre were analyzed
The issue of treatment allocationPROBLEM 2: THERAPEUTIC PROBLEM
The issue of treatment allocation
Kudo M, et al. Dig Dis 2011;29:339–364
SOLUTION 1: INDEPENDENT ALGORITHM
Bolondi L, et al. Sem Liv Dis 2012
The issue of treatment allocation
SOLUTION 2: TREATMENT INDICATIONS
� TREATMENT SELECTION as end-point: multivariate logistic regression models
� SURVIVAL BENEFIT as end-point: multivariate loglogistic parametric survival modelsTreatment selection and survival benefit were combined using Inverse Probability Weight (IPW)
� EVIDENCE BASED approach (literature – multiple societies document)
� New ITA.LI.CA 2015 database including 6669 HCC patients (database ITA.LI.CA. - ItalianLiver Cancer)
�Inclusion criteria:- Cirrhotic patients- Complete follow-up data- Period 2002 – 2015
� Study population: 4867 HCC patients
� External validation cohort: 2651 pts (database from Taipei –Taiwan, 2002-2012)
ITA.LI.CA treatment indications (no algorithm ) were based on:
The issue of treatment allocation
SOLUTION 3: TREATMENT INDICATIONS+DATA BASED SURVIVAL BENEFIT
Variables 0 A B1 B2 B3 C Any
Functional
score (FS)
FS≤ 2: CTP AB and PST 0; CPT ≤ 7 and PST ≤ 2 FS > 2:
CTP C/PST > 2
Diameter (cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any Any
N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any Any
VI / meta no no no no no no no no Intra Extra Any
Median survival71 55 46 33 16 14 8
Therapy
LT
LR
ABL
IAT
SOR
BSC
Therapy
LT
LR
ABL
IAT
SOR
BSC
102
77
64
120
76
61
120
50
64
46
120
33
50
40
120
33
25
28
18
16 7
15
5
102
31
6
Neg
49
21
6
65
Neg
18
0
74
Neg
17
7
87
0
Neg
12
2
0 Neg
1
5
94
Variables 0 A B1 B2 B3 C Any
Functional
score (FS)
FS≤ 2: CTP AB and PST 0; CPT ≤ 7 and PST ≤ 2 FS > 2:
CTP C/PST > 2
Diameter (cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any Any
N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any Any
VI / meta no no no no no no no no Intra Extra Any
Median survival71 55 46 33 16 14 8
LIVER RESECTION
SORAFENIB
LIVER TRANSPLANTATION
ABLATION
TACE/TARE
LT
The issue of treatment allocation
Following ITA.LI.CA indications: 3153 pts (65%)vs 43% BCLC vs 55% HKLC algorithms
In (MS 41 mo)
Out (MS 41 mo)
The issue of treatment allocation
SOLUTION 3: TREATMENT INDICATIONS+DATA BASE SURVIVAL BENEFIT
CONCLUSIONS• There is not worldwide consensus on the best prognostic
system for HCC patients
• The ITA.LI.CA prognostic score showed the best predictive
ability in large western and eastern cohorts
• Beyond BCLC?:
- YES, for intrinsic prognostic pitfals (evidence based and
treatment dependent system)
- YES, for treatment related pitfals (distance from best
clinical practice and personalized approach)
• ITA.LI.CA treatment indications could represent a potential
solution ??
Department of General Surgery and Organ Transplantation,
Hepatobiliary Surgery and Liver Transplantation Unit, University Hospital of Padua, Padua; Italy
Director: Prof. Umberto Cillo
THANK YOU