Integrating Abnormal Prothrombin (PIVKA-II), Alpha-Fetoprotein, and Routine Clinical Variables for Early Detection of Hepatocellular Carcinoma

Summary

This protocol describes a reproducible workflow for integrating PIVKA-II, AFP, and routine clinical variables into a logistic-regression diagnostic model for hepatocellular carcinoma, with validation in early-stage and AFP-negative disease.

Abstract

Early detection of hepatocellular carcinoma (HCC) remains limited by the incomplete sensitivity of alpha-fetoprotein (AFP), especially in early-stage and AFP-negative tumors. This study evaluated whether abnormal prothrombin/protein induced by vitamin K absence-II (PIVKA-II), AFP, and routine clinical variables could be integrated into a reproducible diagnostic model for HCC. A retrospective training cohort of 205 participants, including 112 imaging-confirmed HCC cases and 93 non-HCC controls, was used for model development. An independent prospective validation cohort of 184 participants, including 58 HCC cases and 126 non-HCC controls, was used for out-of-sample testing without model refitting. Pre-imaging blood samples were used for AFP and PIVKA-II measurement, and clinical variables were extracted from the same diagnostic window. The final multivariable logistic-regression model combined ln(AFP), ln(PIVKA-II), age, sex, albumin, international normalized ratio, and platelet count. The combined model achieved an AUC of 0.93 in the training cohort and 0.89 in the validation cohort. In validation, sensitivity was 84.5% and specificity was 90.5%. Sensitivity remained 72.7% in early-stage HCC and 73.1% in AFP-negative HCC, exceeding AFP alone in both subgroups. Calibration and bootstrap validation supported acceptable model stability. These results show how PIVKA-II, AFP, and routinely available clinical variables can be combined into a practical diagnostic triage tool for high-risk liver disease populations.

Introduction

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality and usually arises in patients with chronic liver disease, including hepatitis B virus infection, hepatitis C virus infection, alcohol-related liver disease, and metabolic dysfunction-associated steatotic liver disease¹. Early detection is clinically important because curative treatment is most effective when tumors are found before vascular invasion, extrahepatic spread, or marked deterioration in liver reserve. Current surveillance strategies generally rely on liver ultrasound with or without serum alpha-fetoprotein (AFP)². In routine practic....

Protocol

This study was reviewed and approved by the Ethics Committee of Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University. The approval number was IIT-20230528-0108-01. Written informed consent was obtained from prospectively enrolled participants before blood collection. For retrospectively included clinical records and archived leftover specimens collected during routine care, informed consent was waived by the ethics committee because de-identified data were used and no additional intervention was performed.

Reproducibility, bias control, and data integrity

Eligibi....

Results

Baseline characteristics

The final analysis included 389 participants. The training cohort included 205 participants, comprising 112 imaging-confirmed HCC cases and 93 non-HCC controls. The validation cohort included 184 participants, comprising 58 imaging-confirmed HCC cases and 126 non-HCC controls. Baseline characteristics are summarized in Table 1.

In the training cohort, the 93 non-HCC controls included 30 healthy controls and .......

Discussion

This study evaluated an integrated diagnostic strategy for HCC using PIVKA-II, AFP, and routine clinical variables, with attention to early-stage and AFP-negative disease¹⁴. Across the training and validation cohorts, the combined model outperformed single-marker testing. In the training cohort, the combined model achieved an AUC of 0.93, compared with 0.78 for AFP and 0.85 for PIVKA-II. In the independent validation cohort, the model retained strong discrimination, with an AUC of 0.89, sensitivit.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
1.5 mL nuclease-free microcentrifuge tube	Eppendorf	30123328	Plasma aliquoting and storage
AFP calibrator	Roche Diagnostics	AFP CalSet II, 09227261190	AFP calibration
AFP quality-control material	Roche Diagnostics	PreciControl Tumor Marker, 11776452160	AFP quality control
AFP quality-control material	Roche Diagnostics	PreciControl Universal, 11731416160	AFP quality control
AFP reagent kit	Roche Diagnostics	Elecsys AFP, 09015124190	AFP measurement
Automated electrochemiluminescence immunoassay analyzer	Roche Diagnostics	cobas e 801	AFP and PIVKA-II measurement
Calibration package	R package	rms 6.7–1	Calibration analysis
Decision curve analysis package	R package	rmda 1.6	Decision curve analysis
Hosmer–Lemeshow test package	R package	ResourceSelection 0.3–6	Hosmer–Lemeshow test
K2-EDTA blood collection tube, 10 mL	BD	367525	Peripheral venous blood collection
Multiple-imputation package	R package	mice 3.16.0	Multiple imputation
PIVKA-II reagent kit	Roche Diagnostics	Elecsys PIVKA-II, 08333629190/08333629500	PIVKA-II measurement
Refrigerated centrifuge	Eppendorf	Centrifuge 5425 R	Plasma separation
ROC analysis package	R package	pROC 1.18.5	ROC analysis and DeLong test
Statistical software	R Foundation	R 4.3.2	Statistical analysis
Statistical software interface	Posit Software	RStudio 2023.12.1	Statistical analysis