predictive models for hepatocellular carcinoma

1420 Selected Summaries Gastroenterology Vol. 146, No. 5

RUCHIT SOODALEXANDER C. FORDDepartment of GastroenterologyLeeds Institute of Biomedical and Clinical SciencesLeeds University andLeeds Teaching Hospitals TrustLeeds, United Kingdom

PREDICTIVE MODELS FORHEPATOCELLULAR CARCINOMA

Gavilan JC, Ojeda G, Arnedo R, Puerta S. Predictive factorsof risk of hepatocellular carcinoma in chronic hepatitis C.Eur J Intern Med 2013;24:846–851.

Hepatocellular carcinoma (HCC) is the fifth most com-mon cause of cancer and third leading cause of cancer-related death worldwide. Cirrhosis is the strongest riskfactor for HCC development, and HCC is among the leadingcauses of death among those with cirrhosis. HCC surveil-lance is recommended in high-risk patients, including thosewith cirrhosis, although HCC risk is likely not uniform acrossall patients (Hepatology 2010;53:1–35). Retrospective, case-control studies have identified additional risk factors forHCC, including older age, male gender, diabetes, and alcoholintake. Subsequent studies have developed predictivemodels for HCC using several of these risk factors, althoughmost have been limited by modest accuracy.

In a retrospective cohort study, Gavilan et al evaluatedrisk factors for HCC among 863 patients with hepatitis Cvirus (HCV) infection (Eur J Intern Med 2013;24:846–851).Thirty-four patients (4%) were excluded from analysisbecause they were diagnosed with HCC within 6 months ofinclusion. The remaining 829 patients were followed for anaverage of 82 months (range, 6–216). The relationship ofbaseline variables and HCC risk was assessed using multi-variate Cox regression on a random sample of 75% of thecohort. A risk score for the development of HCC was con-structed based on these variables and then applied to 100%of the original cohort. The accuracy of the derived HCC scorewas estimated using receiver operating characteristic (ROC)curve analysis.

On follow-up, 58 patients developed HCC. Onmultivariateanalysis, independent predictors of HCC included age,platelet count, gammaglobulin, and alpha-fetoprotein (AFP)level. The following factors were used to derive a risk score:3*age þ 1*AFP þ 4.66*gammaglobulin þ 32*(1: plateletcount < 150,000/mL; 0: platelet count > 150,000/mL). Theauthors reported good accuracy, with an area under the curveof 0.80 on ROC curve analysis. This risk score (HCC-4)allowed for stratification of patients into low-, intermediate-,and high-risk populations, with the annual HCC incidencebeing 0.06%, 0.5%, and 2.6%, respectively. They also foundthe average time to develop HCC was significantly shorter inthe high-risk group than the low- and intermediate-riskgroups. The authors concluded that their predictive modelcould be used to identify a high-risk subgroup of patientswithHCV infection, towhom surveillance efforts could be targeted.

Comment. Currently, HCC surveillance is recommended inall patients with cirrhosis and subgroups of patients withhepatitis B virus infection. However, more accurate assess-ment of HCC risk may allow targeted application of HCCsurveillance programs, given that HCC surveillance is onlycost effective among those with an annual HCC risk of>1.5% (Hepatology 2010;53:1–35). Oversurveillance oflow-risk patients leads to unnecessary costs and exposespatients to harm from unnecessary procedures, whereasundersurveillance of high-risk patients leads to late stagetumor detection when curative options no longer exist (Am JGastroenterol 2013;108:425–432). Accurate HCC predictionhas the potential to identify high-risk patients to whomearly detection and/or chemoprevention efforts can betargeted and a group of low-risk patients in whom surveil-lance efforts should be avoided.

The study by Gavilan et al evaluated HCC risk among agroup of HCV patients with various stages of fibrosis (Eur JIntern Med 2013;24:846–851). Using 4 independent vari-ables associated with HCC on multivariate Cox regression,the authors derived the HCC-4 predictive model. The au-thors reported this model had good overall accuracy, usingROC curve analysis and was able to discriminate patientsinto low-, intermediate-, and high-risk subgroups. They alsofound the average time to develop HCC was significantlyshorter in the high-risk group than in the low- andintermediate-risk groups. However, close examination ofthis predictive model is important before routine imple-mentation in clinical practice.

The performance of a predictive model is based on itsability to make accurate predictions as assessed by overallperformance, calibration, discrimination, and reclassifica-tion (Clin Transl Gastroenterol 2014;5:e44). The accuracy ofthe HCC-4 risk score was compared with existing scoresusing ROC curve analysis; however, ROC analysis alone maybe insensitive for comparing predictive models (Clin TranslGastroenterol 2014;5:e44). ROC curves are an appropriatemetric in diagnostic settings when the outcome is deter-mined and can be compared with a gold standard, whereasin predictive models, the outcome has not yet developedand there is a component of randomness at the time ofprediction. Therefore, the use of additional supplementalstatistical methods (eg, misclassification table, HosmerLemeshow test, and Brier score) to characterize qualitativeand quantitative aspects of any prediction model is recom-mended (Clin Transl Gastroenterol 2014;5:e44). Although itis common for prediction research studies to report ROCanalysis results, other measures of model performance,calibration, and reclassification are seldom reported.

Furthermore, the performance of a predictive model isoften substantially greater in derivation datasets than vali-dation sets (BMJ 2009;339:b4184). Given the marked het-erogeneity among at-risk populations in terms of etiologiesof liver disease, degree of liver dysfunction, and prevalenceof other risk factors (such as diabetes, smoking, or alcoholuse), validation of any predictive model for HCC develop-ment is particularly crucial. However, a majority of predic-tion research focuses on model generation, and the needfor validation in independent cohorts remains a pervasive

May 2014 Selected Summaries 1421

deficit (BMJ 2009;339:b4184). Although Gavilan et al per-formed validation, this cohort included the initial derivationcohort and therefore was not independent. It is possible, ifnot likely, that the HCC-4 risk score performance may beoverly optimistic and that it may not perform similarly wellin a new patient population. The importance of validation inan independent cohort was highlighted in a recent study,which demonstrated the HALT- model had substantiallylower discriminatory power (c-statistic 0.60) and diagnosticaccuracy when externally validated (Am J Gastroenterol2013;108:1723–1730).

Although cirrhosis is the strongest risk factor for HCCdevelopment, stage of fibrosis was not included in the HCC-4predictive model. However, this may be an advantage; pre-dictive models are more useful when only including readilyavailable factors, and stage of fibrosis is not always known inpatientswith liver disease. In fact,�20%of patientsmayhaveunrecognized cirrhosis at the time of HCC presentation,contributing to HCC surveillance underutilization (ClinGastroenterol Hepatol 2013;11:472–477; Cancer Prev Res(Phila) 2012;5:1124–1130). Furthermore, HCC can stilldevelop in patients without overt cirrhosis. In the HALT-Ccohort, 3.9% of patients with significant fibrosis but notcirrhosis developed HCC (Gastroenterology 2009;136:138–148). Similarly, there are increasing case reports of HCCin patients with nonalcoholic steatosis but without cirrhosis(Clin Gastroenterol Hepatol 2012;10:1342–1359 e2). Furtherstudies are needed to identify a high-risk subgroup of non-cirrhotic patients who might benefit from surveillance.

It is possible that predictive models using clinical vari-ables alone may have insufficient accuracy so novel ap-proaches, such as the addition of genetic markers orbiomarkers, are needed to improve model performance(Am J Gastroenterol 2013;108:1723–1730). For example,PNPLA3 is a well-described genetic risk factor for HCCdevelopment among patients with nonalcoholic steatosiscirrhosis; however, studies are needed to determine itssignificance when added to other clinical variables in

predictive models (Dig Liver Dis 2013;45:619–624; Am JGastroenterol 2014;109:325-334.). Similarly, studies havedemonstrated that AFP levels are predictive of subsequentHCC development, but research is needed to evaluateother novel biomarkers, such as DCP and AFP-L3, as well astheir significance in predictive modeling (Am J Gastro-enterol 2013;108:1723–1730; Clin Gastroenterol Hepatol2013;11:437–440).

Overall, HCC prediction models have been proposed withthe end goal of formulating a surveillance plan personalizedfor individual patients based on risk stratification. The studyby Gavilan et al adds to this literature, specifically examiningthis topic among HCV patients with a range of fibrosisstages. With continued refinement, accurate identification ofhigh- and low-risk patients can facilitate targeting of sur-veillance and/or chemoprevention efforts. Future effortsshould be made to reevaluate and externally validate HCCpredictive models, such as HCC-4, because this approachmay salvage data that can be utilized in future endeavors.

MARIAM NAVEEDDivision of Digestive and Liver DiseasesDepartment of Internal MedicineUniversity of Texas Southwestern Medical CenterDallas, Texas

AKBAR K. WALJEEDivision of Gastroenterology and HepatologyDepartment of Internal MedicineUniversity of Michigan Medical CenterAnn Arbor VA Healthcare SystemAnn Arbor, Michigan

AMIT G. SINGALLiver Tumor ClinicDivision of Digestive and Liver DiseasesDepartment of Internal MedicineUniversity of Texas Southwestern Medical CenterDallas, Texas