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Updated: May 3, 2026
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
A new laboratory-based algorithm to predict development of hepatocellular carcinoma in patients with hepatitis C and
Hashem B El-Serag1, Fasiha Kanwal1, Jessica A Davila2
1Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center, Houston, Texas; Houston VA Health Services Research and Development Service Center of Excellence, Michael E. DeBakey VA Medical Center, Houston, Texas; Baylor College of Medicine, Houston, Texas.
Background & Aims:
Serum levels of α-fetoprotein (AFP) are influenced not only by the presence of hepatocellular carcinoma (HCC), but also by the underlying severity and activity of liver disease, which is reflected by liver function tests. We constructed an AFP-based algorithm that included these factors to identify patients at risk for HCC, and tested its predictive ability in a large set of patients with cirrhosis.
Methods:
We used the national Department of Veterans Affairs Hepatitis C Virus Clinical Case Registry to identify patients with cirrhosis, results from at least 1 AFP test, and 6 months of follow-up. Our algorithm included data on age; levels of aspartate aminotransferase, alanine aminotransferase (ALT), alkaline phosphatase, total bilirubin, albumin, creatinine, and hemoglobin; prothrombin time; and numbers of platelets and white cells. We examined the operating characteristics (calibration, discrimination, predictive values) of several different algorithms for identification of patients who would develop HCC within 6 months of the AFP test. We assessed our final model in the development and validation subsets.
Results:
We identified 11,721 patients with hepatitis C virus-related cirrhosis in whom 35,494 AFP tests were performed, and 987 patients developed HCC. A predictive model that included data on levels of AFP, ALT, and platelets, along with age at time of AFP test (and interaction terms between AFP and ALT, and AFP and platelets), best discriminated between patients who did and did not develop HCC. Using this AFP-adjusted model, the predictive accuracy increased at different AFP cutoffs compared with AFP alone. At any given AFP value, low numbers of platelets and ALT and older age were associated with increased risk of HCC, and 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 70 years 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.
Conclusions:
We developed and validated an algorithm based on levels of AFP, platelets, and ALT, along with age, which increased the predictive value for identifying patients with hepatitis C virus-associated cirrhosis likely to develop HCC within 6 months. If validated in other patient groups, this model would have immediate clinical applicability.
