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PIVKA-II-Based Model IDs Liver Cancer Risk in Hep C-Related Cirrhosis

Medically reviewed by Carmen Pope, BPharm. Last updated on Dec 5, 2024.

By Elana Gotkine HealthDay Reporter

THURSDAY, Dec. 5, 2024 -- Among patients with hepatitis C virus (HCV)-related cirrhosis with sustained virological response (SVR) to direct-acting antivirals (DAAs), a serum protein induced by vitamin K absence or antagonist-II (PIVKA-II)-based model is accurate for predicting hepatocellular carcinoma (HCC) risk, according to a study published online Nov. 21 in Alimentary Pharmacology & Therapeutics.

Gian Paolo Caviglia, Ph.D., from the University of Turin in Italy, and colleagues developed and validated a PIVKA-II-based model for HCC risk stratification in patients with HCV-related cirrhosis with SVR to DAAs. Data were included for 1,220 patients (Turin cohort, 531; Pisa cohort, 335; and Milan cohort, 354).

The researchers derived a model including PIVKA-II combined with age, sex, alanine transaminase, aspartate aminotransferase, gamma-glutamyl transferase, platelet count, albumin, and total bilirubin from the training cohort (Turin + Pisa), with a C-index of 0.72. The models showed a C-index of 0.71 in the validation cohort (Milan), with an area under the curve of 0.84 for identifying patients who developed HCC within 12 months of follow-up. The cumulative incidence of HCC was 2.7, 4.0, and 14.3 percent in the low-, medium-, and high-risk groups when the patients were categorized into three risk categories. In the low-risk group, no HCC occurred within three years of follow-up.

"Our model allowed the identification of patients at low risk of HCC development that may not need surveillance, reducing costs and harms from surveillance," the authors write.

Several authors disclosed ties to the pharmaceutical industry.

Abstract/Full Text

Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.

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