Development and Internal Validation of a Model for Early Detection of Hepatocellular Carcinoma in Patients With Cirrhosis

Background:

Surveillance for hepatocellular carcinoma (HCC) is recommended in patients with cirrhosis; however, early detection efforts are limited by suboptimal effectiveness.

Aim:

To derive and validate a model to accurately distinguish cirrhotic patients with and without HCC and compare the accuracy of the model to that of α-fetoprotein (AFP) alone.

Methods:

We conducted a case-control study of cirrhotic patients with and without HCC seen at a large urban hospital system between January 2005 and June 2012. We derived multivariate logistic regression models for the presence of HCC and early-stage HCC. Discriminatory power was evaluated using receiver operating characteristic curve analysis in derivation and validation cohorts using a 10-fold cross-validation approach.

Results:

We identified 1356 patients with cirrhosis, with (n=455, 147 early stage) and without (n=901) HCC. We found that AFP>20 ng/mL and FIB-4, a noninvasive marker of fibrosis, were significantly associated with the presence of HCC (OR=10.5; 95% CI, 7.9-13.9 and OR=1.05; 95% CI, 1.03-1.07, respectively) and early-stage HCC (OR=4.4; 95% CI, 2.9-6.5 and OR=1.06; 95% CI, 1.03-1.09, respectively). Models incorporating AFP and FIB-4 had good discriminatory power, with c-statistics of approximately 0.80, in both derivation and validation cohorts. The model for early-stage HCC had higher discriminatory power than AFP alone (c-statistic 0.73; 95% CI, 0.69-0.78) in derivation and validation cohorts (P=0.02 and 0.15, respectively).

Conclusions:

Models including AFP and FIB-4 can accurately discriminate cirrhotic patients with early-stage HCC from those without HCC.