How to apply HCC prediction models to practice?

How to apply HCC prediction models to practice? Department of Internal Medicine, Keimyung University School of Medicine Woo Jin Chung

HCC prediction models 독특하게간세포암환자들의생존은암의진행상태뿐아니라기저간기능의중증정도에영향을받는특성이있다. 임상에서쉽게적용이가능한유용한예측모델에관한요구가있고이에대해다양한예측모델들이보고되고있다. 이러한예측모델들은 1) 사용되는예측인자들이언제나재현이가능해야하며, 2) 쉽게임상에서적용이가능하며, 3) 임상적으로의미있는결과를유출할수있어야한다. Objective parameters Subjective parameters Parameters readily available Prospectively designed Validated Prediction models?????

HCC prediction models: HCC Risk Laboratory based algorithm to predict HCC 2014 Gastroenterology HCC risk prediction scores 2014 Gut ADRESS-HCC Risk model 2014 Cancer Clinical scoring system 2014 PLoS ONE Transient elastography based risk estimation 2014 Oncotargets Ther Prediction models risk score 2013 Hepatology Prediction model by Japanese PHC 2012 Preventive Medicine Data mining model 2012 J Hepatol

HCC prediction models: Survival Criteria specific long-term survival prediction model after LT 2015 Scientific reports DCE-MRI kinetic parameters as prognostic biomarkers 2015 Acad Radiol Prognostic model based on Bayesian Network after hepatectomy 2015 PLoS ONE K-MESIAH 2015 PLoS ONE Score model for survival after LT 2015 HBPD int Artificial Neural Network model 2012 PLoS ONE MESIAH 2012 Hepatology

HCC prediction models: Prognosis Optimal cutoff of serum afp. 2015 PLoS ONE Predicting prognosis after curative surgery 2009 BMC Cancer HCC prediction models: Metastasis microrna-based prediction model 2015 Oncotarget Clinicopathological model to predict bone metastasis 2011 J Cancer Res Clin Oncol

HCC prediction models: Microvascular invasion Preoperative estimation of microvascular invasion risk 2015 JAMA Surg Preoperative prediction of tumor grade and microvascular invasion 2010 J Hepatol HCC prediction models: Disease-free survival Prediction by gene expression profiling 2013 Ann Surg Oncol

HCC prediction models: Mortality Mortality risk after LT 2012 Surgery MELD 2003 Gastroenterology HCC prediction models: Recurrence Platelet count before therapy. 2015 WJG Preoperative scoring model after resection or LT 2015 HBPD Int Genomic predictor models 2014 PLoS Medicine

Laboratory based Algorithm to Predict HCC: In patients with Hepatitis C and LC 50대 70대 N=11,721 At any given AFP value, low numbers of platelets and ALT and older age were associated with increased risk of HCC, whereas high levels of ALT and normal/high numbers of platelets were associated with low risk for HCC. For example, the probabilities of HCC, based only on 20 ng/ml and 120 ng/ml AFP, were 3.5% and 11.4%, respectively. However patients with the same AFP values (20 ng/ml and 120 ng/ml) who were 70y old, with ALT levels of 40 IU/ml and platelet counts of 100,000, had probabilities of developing HCC of 8.1% and 29.0%, respectively. El-Serag HB et al, Gastroenterology 2014

ADRESS-HCC Risk Model: Risk Prediction of HCC in Patients with Cirrhosis N=34,932 An ADRESS-HCC score of 4.67 identified those whose risk of HCC was 1.5% per year. No patient with an ADRESS-HCC score of <2.15 (80 patients) developed HCC during the follow-up period. Conclusions: The ADRESS-HCC risk model is a useful tool for predicting the 1-year risk of HCC among patients with cirrhosis. ADRESS: age, diabetes, etiology of cirrhosis, sex, and severity Flemming JA, Norah Terrault et al, Cancer 2014

Clinical Score System for Predict Risk of HCC: Asymptomatic anti-hcv(+) from REVEAL HCV cohort N=975 & 572 The predicted risks for HCC were estimated by sum of risk scores by the equation: P 0 was the baseline disease free probability, βi was the regression coefficient for the ith variables (Xi), Mi denoted the mean level of Xi 1. all anti-hcv seropositives (risk score <13 for low-risk, 13 18 for medium-risk, 19 for high-risk group) 2. anti-hcv seropositives with detectable HCV RNA (risk score <9 for low-risk, 9 15 for medium-risk, 15 for high-risk group). Conclusions: Scoring systems for predicting HCC risk of HCVinfected patients had good validity and discrimination capability, which may triage patients for alternative management strategies. Lee MH, Chen CJ et al, Hepatology 2013 -> Lee MH, Chen CJ et al, PLoS ONE 2014

Transient elastography based risk estimation of HCC: Predictive model for HBV related occurrence of HCC N=1,250 3-year probability of HCC occurrence The predicted risk of occurrence of HCC calibrated well with the observed risk, with a correlation coefficient of 0.905 (P<0.001). Conclusions: This novel model accurately estimated the risk of HCC occurrence in patients with chronic hepatitis B Kim DY, Han KH et al, Oncotargets and Ther 2014

Prediction Model for 10yr Risk of HCC: In middle aged Japanese by JPHC based Prospective Study Cohort N=17,654 Those subjects with total scores of 17 or more under this system (score range: 1 to 19) had an estimated 10-year HCC risk of over 90%; those with 4 points or less had an estimated risk of less than 0.1%. Michikawa T, Manami Inoue et al, Preventive Medicine 2012

Data Mining Model to Identify Risk of HCC: To identify patients at high risk for HCC in CHC HCC 누적발생 N=1,003 IFN 치료로 SVR 후 HCC 누적발생 High -> Intermediate -> Low risk HCC risk group / % 5 year 10 year 누적발생 SVR (+/-) 누적발생 High and intermediate risk 11.6% 4.5 vs 9.5% 24.5% Low risk 1.0% 0.9 vs 1.8% 4.8% Conclusions: The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC. Kurosaki M, Namiki Izumi et al, J Hepatol 2012

AATH Model for Prediction of Survival : Dynamic contrast-enhanced MRI kinetic Parameters as Prognostic Biomarkers N=20 Arterial flow fraction Fractional interstitial volume The adiabatic approximation to the tissue homogenicity (AATH) model-derived PS was an effective prognostic biomarker for both 1YS and OS. Permeability-surface area product Lee SH, hiroyuki Yoshida et al, Academic Radiology 2015;22(11):1344-1360.

MESIAH: Model to Estimate Survival in Ambulatory patients with HCC N=477 & 904 Risk score 25, 75 th percentile 기준으로 Tier 1, 2, 3 로분류 Conclusions: A new model to predict survival of HCC patients based on objective parameters provides refined prognostication and supplements the BCLC classification. Yang JD, W. Ray Kim et al, Hepatology 2012

K-MESIAH: Korean version of Model to Estimate Survival in Ambulatory patients with HCC N=1,969 Risk probability Conclusions: A survival prediction model for Korean HCC patients was developed and validated to have a high level of performance. This K-MESIAH may be more useful in clinical practice and personalized care in a hepatitis B virus endemic area. Nam BH, Park JW et al, PLoS ONE 2015

Score Model for Survival after LT: A score model for predicting post-lt survival in HBV LC related HCC recipients The score model was as follows: N=238 0.114 (Child-Pugh score)-0.002 (positive HBV DNA detection time)+ 0.647 (number of tumor nodules)+0.055 (max diameter of tumor nodules)+ 0.231 lnafp+0.437 (tumor differentiation grade). AUROC 0.887 The receiver operating characteristic curve analysis showed that the area under the curve of the scoring model for predicting the post-lt survival was 0.887. The cut-off value was 1.27, which was associated with a sensitivity of 72.5% and a specificity of 90.7%, respectively. Wang LY et al, HBPD Int 2015;14:43-49

MELD: Model for End-Stage Liver Disease MELD Score = 10 * ((0.957 * ln(creatinine)) + (0.378 * ln(bilirubin)) + (1.12 * ln(inr))) + 6.43 MELD calculator Weisner R, Ray Kim et al Gastroenterology 2003

Predicting Prognosis with Common Clinicopathologic Parameters: In HCC patients after curative surgery N=572 Survival Disease free Survival 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). Hao K, Luk JM et al, BMC Cancer 2009

Prediction model for LN Metastasis: A microrna-based prediction model N=192 Five LNM associated predictive factors: vascular invasion, BCLC stage, mir-145, mir-31, and mir-92a. The cutoff value 4 was used to distinguish high and low- risk groups. The model sensitivity and specificity was 69.6 and 80.2%. And the area under the curve (AUC) for the mirna-based prognostic model was 0.860. The 5-year positive and negative predictive values of the model in the validation cohort were 30.3 and 95.5 %, respectively. Cox regression analysis revealed that the LNM hazard ratio of the high-risk versus low-risk groups was 11.751 (95 % CI, 5.110 27.021; P < 0.001) in the validation cohort. Conclusion, the mirna-based model is reliable and accurate for the early prediction of LNM in patients with HCC. Zhang L, Xiang ZL et al, Oncotarget 2015

Clinocopathological Model to Predict Bone Metastasis: N=179 A cutoff value of 9.4 best discriminated BM risk and was able to exclude future BM development with high accuracy in the validation cohort. The sensitivity and specificity were 73.7 and 78.7%, respectively. The 1- and 2-year cumulative BM rates were 10.8% and 27.4% in the high-risk group and 2.4 and 4.3% in the low-risk group. AUROC 0.762 The hazard ratio for BM of the high versus low-risk group was 9.240 (95% CI: 3.319 25.722). Conclusion: The simple prediction model constructed from clinicopathological parameters is accurate in predicting BM development in HCC patients. Xiang ZL et al, J Cancer Res Clin Oncol 2011

Model for Preoperative Estimation of MVI: Nomogram in HBV related HCC within Milan Criteria N=1004 Patients who had a nomogram score of less than 200 or 200 or greater were considered to have low or high risks of MVI presence, respectively. Conclusions: The nomogram achieved an optimal preoperative prediction of MVI in HBV-related HCC within the Milan criteria. Using the model, the risk for an individual patient to harbor MVI can be determined, which can lead to a rational therapeutic choice Lei Z, Feng Shen et al, JAMA Surg 2015

Gene Expression Profiling for Prediction of Ds-free Survival N=286 & 83 Good survival vs Poor survival The estimated 5-year DFS rate for poor-survival patients (n = 41) was 24.1 %compared to 55.3 % for good survival patients (n = 42) (hazard ratio for DFS = 2.375, 95 %confidence interval 1.34 4.21). Good survival Poor survival Stage I Stage II/III Stage I Stage II/III DFS rates 59.1% 52.4% 46.2% 12.3% Multivariate analysis in the validation set showed that DFS gene signature of tumor was an Independent predictor of shorter DFS (P = 0.018). Lim HY, Park CK et al, Ann Surg Oncol 2013

Predicting Risk of HCC Recurrence : Thrombocytopenia for prediction of HCC recurrence a cutoff value of 100 10 9 /L for PLT N=4163 HR = 1.42, 95%CI: 1.27-1.60 In conclusion, our meta-analysis suggested that thrombocytopenia was a valuable, inexpensive predictor for recurrence in patients with HCC. Pang Q et al, World J Gastroenterol 2015;21(25):7895-7906.

Score Model for Recurrence after Resection or LT: A 3-factor preoperative scoring model predict risk of recurrence Recurrence risk score model was as follows: 0.758 HBsAg status (negative: 0; positive: 1)+0.387 plasma fibrinogen level ( 3.24 mg/dl: 0; >3.24 mg/dl: 1)+0.633 TTV ( 107.5 cm3: 0; >107.5 cm3: 1). TTV: total tumor volume. N=238 AUROC 0.658 The cut-off value was set to 1.02, and we defined the patients with the score 1.02 as a low risk group and those with the score >1.02 as a high risk group. The 3-year recurrence-free survival rate was significantly higher in the low risk group compared with that in the high risk group (67.9% vs 41.3%, P<0.001) Recurrence risk scoring model is effective in categorizing recurrence risks and in predicting recurrence-free survival of HCC before potential surgical curative treatment. Wang LY, Jiang Nan et al, HBPD Int 2015

Genomic Predictor Model for Recurrence of HCC: N=72 In multivariate analysis, the 65-gene risk score was the strongest risk factor for very early recurrence (1 y after surgical resection) (hazard ratio, 1.7; 95% confidence interval, 1.1 2.6; p= 0.01). The potential significance of STAT3 activation in late recurrence was predicted by gene network analysis and validated later. We also developed and validated 4- and 20-gene predictors from the full 233-gene predictor. Conclusions: Two independently developed predictors reflected well the differences between early and late recurrence of HCC at the molecular level and provided new biomarkers for risk stratification. Kim JH et al, PLoS Med 2014

결론 대부분의간세포암예측모델은치료전초기검사를기반으로하고있으며, 치료후적용하기에는비교적제한적이다. 간세포암, 특이진행된간세포암의임상경과에서치료법및치료결과가환자 의생존에미치는영향이지대하므로치료후인자를통한예측모델이더욱 요구된다.