Title : Validation of GLAS (GP73+LG2m+Age+Sex) and ASAP (Age+Sex+AFP+PIVKA-II) algorithms for the management of liver fibrosis, cirrhosis and cancer
Abstract:
Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but it is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. We tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with early-stage liver disease and healthy individuals in two independent cohorts. In a validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. The GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.
The biomarkers AFP and PIVKA-II may be useful for detecting early-stage HCC. We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. In a case-control study, serum AFP and PIVKA-II were measured using an automated immunoassay analyzer system in a pilot cohort. Five predictive models for detecting HCC were developed based on Age, Sex, AFP, and PIVKA-II (ASAP); the best model was validated using the ASAP algorithm in an independent cohort from the Early Detection Research Network (EDRN). In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI: 0.93-0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI: 0.85-0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81-0.88), sensitivity was 70%, and specificity was 86%. Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.
Keywords: Golgi Protein 73 (GP73), Laminin Gamma 2 monomer (LG2m), Protein induced by vitamin K absence-II (PIVKA-II), Alpha-fetoprotein (AFP), Liver Disease Management, Algorithm, hepatocellular carcinoma (HCC)
