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 practice, however, surveillance performance is affected by operator dependence, small tumor size, heterogeneous cirrhotic nodules, obesity-related imaging difficulty, and tumors that produce little or no AFP^3,4.

AFP remains widely used because it is inexpensive, accessible, and familiar to clinicians. Its diagnostic value is limited by incomplete sensitivity and imperfect specificity. At the conventional threshold of 20 ng/mL, AFP can miss a substantial proportion of early-stage and AFP-negative HCC. Lower thresholds may improve sensitivity but increase false-positive results in patients with active hepatitis, cirrhosis, or other inflammatory liver conditions^5,6. These limitations make single-marker interpretation insufficient for diagnostic triage in high-risk liver disease populations.

Abnormal prothrombin, also known as protein induced by vitamin K absence-II (PIVKA-II) or des-γ-carboxy prothrombin, is released when malignant hepatocytes produce incompletely carboxylated prothrombin. PIVKA-II reflects coagulation-related tumor biology rather than AFP secretion and may remain informative when AFP is low⁷. Previous studies and meta-analyses have shown that PIVKA-II improves HCC detection, particularly when combined with AFP, and may add value in early-stage or AFP-negative disease^8,9. Marker-only strategies, however, do not fully account for host background, liver reserve, platelet count, coagulation status, or underlying liver disease.

Composite diagnostic models offer a practical way to interpret tumor-marker results together with clinical context. GALAD, GAAD, and related models have shown that demographic variables and biomarkers can be combined to improve HCC detection across different etiologies and populations^10,11. Their performance supports the use of multivariable risk estimation rather than isolated biomarker thresholds. At the same time, some models include assays that are not available in all laboratories, and cutoff behavior may vary across regions, etiologic profiles, disease stages, and intended clinical use¹². A model built from widely available biomarkers and routine laboratory variables may be easier to implement in ordinary diagnostic workflows. Unlike previously published models that may rely on specialized biomarkers or population-specific cutoff strategies, the present study evaluated a multivariable diagnostic model based on widely available biomarkers and routine clinical variables and validated its performance in an independent prospective cohort.

PIVKA-II is suited to this framework because it complements AFP and can be measured on automated immunoassay platforms already used in clinical laboratories. Routine variables such as age, sex, albumin, bilirubin, platelet count, international normalized ratio, and liver disease background are available at the same clinical encounter and may capture liver-reserve or coagulation-related information relevant to HCC risk¹³. Combining these variables with AFP and PIVKA-II may improve discrimination while avoiding dependence on a single cutoff.

PIVKA-II, AFP, and routine clinical variables were therefore assessed using a dual-cohort diagnostic design. A multivariable logistic-regression model was developed in a retrospective training cohort and tested in an independent prospective validation cohort without refitting. Diagnostic performance was compared with AFP, PIVKA-II, and clinical variables alone, with predefined attention to early-stage HCC, AFP-negative HCC, cutoff interpretation, calibration, and bootstrap internal validation.

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

Eligibility criteria, blood-sampling timepoints, imaging adjudication rules, subgroup definitions, candidate predictors, cutoff definitions, missing-data rules, and validation procedures were prespecified before statistical analysis. Blood samples were collected before contrast-enhanced imaging confirmation and before any anti-tumor treatment. Laboratory testing used anonymized study identifiers, and samples from the training and validation cohorts were tested in randomized order. Laboratory personnel were blinded to final diagnosis, cohort assignment, Barcelona Clinic Liver Cancer (BCLC) stage, and AFP-negative status. Imaging diagnosis and staging were abstracted independently from laboratory testing. Data entry, unit conversion, cohort assignment, exclusion coding, missing-value coding, and statistical datasets were checked by two investigators before model fitting. The training and validation cohorts were kept separate throughout variable screening, model fitting, cutoff derivation, and validation.

Study population and design

This dual-cohort diagnostic study developed and validated a multivariable model for HCC detection using abnormal prothrombin/protein induced by PIVKA-II, AFP, and routine clinical variables. The retrospectively assembled training cohort included participants assessed between January 1, 2021 and December 31, 2023. This cohort was used for variable screening, model fitting, cutoff derivation, and bootstrap internal validation. The prospectively enrolled validation cohort included participants assessed between January 1, 2024 and December 31, 2025. This cohort was used for out-of-sample testing only. No variable reselection, model refitting, coefficient updating, cutoff re-optimization, or subgroup-based recalibration was performed in the validation cohort. Participant flow, cohort assignment, blood sampling, plasma processing, biomarker testing, imaging adjudication, and statistical workflow are shown in Figure 1.

Participants were recruited from a high-risk liver disease surveillance or diagnostic pathway at Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University. High-risk status was defined as chronic hepatitis B infection, chronic hepatitis C infection, cirrhosis of any etiology, or fatty liver disease with documented fibrosis based on clinical assessment, laboratory records, or prior imaging. Eligible participants were adults aged ≥18 years who completed pre-imaging blood sampling and contrast-enhanced multiphasic computed tomography (CT) and/or dynamic contrast-enhanced magnetic resonance imaging (MRI) within 14 days after blood collection. Exclusion criteria were previous HCC treatment, including resection, ablation, transarterial therapy, or systemic therapy; vitamin K administration within 7 days before sampling; vitamin K antagonist therapy or other anticoagulant therapy likely to affect coagulation interpretation; acute cholangitis or biliary obstruction at sampling; pregnancy; missing AFP, PIVKA-II, or final imaging diagnosis; and unacceptable specimen quality without a valid repeat sample.

The final study population 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. In the training cohort, the 93 non-HCC controls included 30 healthy controls and 63 chronic liver disease controls. In the validation cohort, the 126 non-HCC controls included 0 healthy controls and 126 chronic liver disease controls.

Symptomatic status was not assigned from hepatitis history or compensated cirrhosis alone. A participant was classified as symptomatic only when the pre-imaging record documented new or worsening right upper quadrant pain, unexplained weight loss, abdominal distension, jaundice, dark urine, or clinically documented decompensation not explained by another confirmed cause. Participants without these findings were classified as asymptomatic or surveillance-detected.

Diagnostic adjudication and staging

Final HCC status was adjudicated using contrast-enhanced multiphasic CT and/or dynamic contrast-enhanced MRI performed in routine care. Imaging was required to include at least an arterial phase and a portal venous phase; delayed-phase findings were incorporated when available. A lesion was classified as HCC when imaging in an at-risk liver showed arterial-phase hyperenhancement with portal venous and/or delayed-phase washout, with or without an enhancing capsule. Participants without imaging features consistent with HCC and without another malignant diagnosis were classified as non-HCC.

Imaging information was abstracted with a prespecified form recording imaging modality, phases obtained, arterial-phase hyperenhancement, washout, capsule appearance, number of lesions, maximal lesion diameter, macrovascular invasion, and extrahepatic spread. Two trained readers independently abstracted imaging features from final radiology reports. Disagreements were resolved by consensus using the same predefined criteria. If both CT and MRI were available, the modality with the most complete multiphasic characterization was used. If both modalities were complete, the modality documented as the basis for the final clinical decision was used.

BCLC stage was assigned after HCC status had been finalized. Early-stage HCC was defined as BCLC stage 0–A. AFP-negative HCC was defined as AFP <20 ng/mL at the pre-imaging blood draw. These subgroup labels were used for subgroup performance evaluation, not for model training or participant eligibility.

Sample collection and plasma processing

Peripheral venous blood was collected in the morning after an overnight fast of at least 8 h, before contrast-enhanced CT/MRI confirmation and before anti-tumor therapy. A total of 10 mL of venous blood was drawn into K₂-EDTA anticoagulant blood collection tubes. Tubes were inverted 8–10 times immediately after collection and transported to the laboratory at 4 °C. The interval from venipuncture to centrifugation was kept within 2 h and recorded in the sample log.

Plasma was separated by centrifugation at 1,600 × g for 10 min at 4 °C using a refrigerated centrifuge. Plasma supernatant was transferred into 1.5 mL nuclease-free microcentrifuge tubes and aliquoted into 0.5–1.0 mL portions. Aliquots were stored at -80 °C until testing. Before biomarker measurement, one aliquot was thawed once at room temperature, 20–25 °C, for 20–30 min, mixed gently by inversion, and tested within 2 h after complete thawing. Samples with visible clotting, moderate-to-severe hemolysis, or marked lipemia were rejected according to predefined laboratory acceptance criteria. For the prospective validation cohort, repeat blood collection was performed before imaging confirmation when clinically feasible.

AFP and PIVKA-II measurement

AFP and PIVKA-II were measured using electrochemiluminescence immunoassays on an automated immunoassay analyzer. AFP was recorded in ng/mL, and PIVKA-II was recorded in mAU/mL. Results were exported directly from the laboratory information system.

Each analytical batch included assay-specific calibrators and at least two internal quality-control levels. Batch acceptance required all quality-control results to fall within the predefined laboratory control range. Analytical precision targets were within-run coefficient of variation ≤10% and between-run coefficient of variation ≤15% for both AFP and PIVKA-II. If quality control failed or batch drift was detected, the batch was rejected, the analyzer was recalibrated, and the batch was repeated when sufficient sample volume remained. For an implausible or out-of-range single-sample result, two repeat measurements were performed from the same thawed aliquot when possible, and the mean of the two accepted repeat values was used. If repeat testing failed or sample volume was insufficient, the result was marked invalid and handled according to the missing-data rule.

Routine clinical variable extraction

Routine clinical variables were extracted from the same clinical encounter as the pre-imaging AFP/PIVKA-II blood draw. Candidate variables included age, sex, alanine aminotransferase, aspartate aminotransferase, albumin, total bilirubin, international normalized ratio (INR), prothrombin time, and platelet count. When multiple values were available within the pre-imaging window, the value closest to the AFP/PIVKA-II sampling time was selected. Units were standardized before analysis. Age was recorded in years; sex was coded as female = 0 and male = 1; platelet count was recorded as ×10⁹/L; albumin was recorded as g/L; total bilirubin was recorded as μmol/L; AFP was recorded as ng/mL; and PIVKA-II was recorded as mAU/mL.

Range checks, unit checks, and implausible-value checks were performed after extraction. Flagged values were verified against source records. A value was excluded only when the source record confirmed a specimen-quality problem, unit-conversion error, or processing deviation. Verified extreme values were retained. The variable dictionary defined dataset structure, variable names, units, coding rules, transformation rules, and missing-value codes.

Predictor definition and variable selection

Candidate predictors were defined before model development. Core biomarkers were AFP and PIVKA-II. Routine clinical candidates were age, sex, alanine aminotransferase, aspartate aminotransferase, albumin, total bilirubin, INR or prothrombin time, and platelet count. AFP and PIVKA-II were natural-log transformed. Values below the assay reporting limit were replaced by one-half of the lower reporting limit before transformation.

Univariate comparisons between HCC and non-HCC participants in the training cohort used independent-sample t tests for approximately normally distributed variables, Mann-Whitney U tests for skewed continuous variables, and χ2 or Fisher's exact tests for categorical variables. Variables with P < 0.10 in univariate screening were considered for multivariable modeling. Multicollinearity was assessed using variance inflation factor (VIF), and VIF >5 was treated as concerning collinearity. When two variables were strongly collinear, the variable with clearer clinical interpretability and better incremental model performance was retained.

The final multivariable logistic-regression model was developed only in the training cohort. ln(AFP) and ln(PIVKA-II) were forced into the model. Additional predictors were selected by backward elimination guided by Akaike information criterion. After backward selection, the retained variables were ln(AFP), ln(PIVKA-II), age, sex, albumin, INR, and platelet count.

Model development and risk-score calculation

The diagnostic model was fitted using multivariable logistic regression with HCC status as the binary outcome. The predicted probability of HCC was calculated from the linear predictor η using the following equation:

p(HCC)=1/[1+exp(-η)]

η = -4.862+0.247×ln(AFP)+0.712×ln(PIVKA-II)+0.018×age+0.331×sex-0.062×albumin+0.554×INR-0.004×plateletcount

AFP was measured in ng/mL, PIVKA-II in mAU/mL, age in years, sex was coded as female = 0 and male = 1, albumin was measured in g/L, INR was unitless, and platelet count was measured as ×109/L. Natural logarithms were used for AFP and PIVKA-II. A nomogram was generated from the same fitted coefficients. Predicted probability, not the nomogram point scale, was used for receiver operating characteristic (ROC) analysis, calibration analysis, decision curve analysis, and validation.

Cutoff definitions and threshold handling

PIVKA-II thresholds were labeled according to analytic purpose. The established clinical cutoff for PIVKA-II was 40.0 mAU/mL. The training-cohort Youden cutoff for PIVKA-II was 45.0 mAU/mL. The fixed-specificity cutoff was derived by selecting the threshold closest to 90% specificity in the training cohort; this yielded 38.0 mAU/mL for PIVKA-II. A validation-only apparent threshold of 37.5 mAU/mL was treated as exploratory and was not used as the primary validation cutoff.

For the combined model, the primary binary cutoff was derived from the training cohort using the Youden index and applied unchanged to the validation cohort. AFP used the established clinical cutoff of 20.0 ng/mL for AFP-negative subgroup definition and routine-practice comparison. Optimized AFP cutoffs used in ROC analysis were labeled separately from the clinical threshold. Cutoffs were therefore reported by analytic purpose rather than treated as interchangeable thresholds.

Model performance, calibration, and validation

Discrimination was evaluated using receiver operating characteristic curves and area under the curve. AUCs were reported with 95% confidence intervals. AUC comparisons used the DeLong test. Classification performance was reported as sensitivity, specificity, positive predictive value, negative predictive value, and accuracy at prespecified cutoffs.

Calibration was evaluated with calibration intercept, calibration slope, Brier score, and the Hosmer–Lemeshow goodness-of-fit test. Calibration plots compared predicted and observed HCC probabilities across risk groups. Internal validation used 1,000 bootstrap resamples in the training cohort to estimate optimism-corrected AUC and optimism-corrected calibration slope. The validation cohort was used for external performance assessment only. No coefficient updating, model refitting, threshold re-optimization, or subgroup-based recalibration was performed in the validation cohort.

Clinical utility was assessed using decision curve analysis across threshold probabilities from 0.05 to 0.50. Net benefit was compared for AFP alone, PIVKA-II alone, the clinical-variable model, and the combined model.

Missing-data handling and sensitivity analysis

The primary analysis used complete-case modeling for participants with all variables retained in the final model. Missing values were summarized for each candidate predictor. If missingness exceeded 5% for any retained predictor, multiple imputation by chained equations was performed as a sensitivity analysis using the outcome, all retained predictors, and cohort indicator in the imputation model. Variables with missingness greater than 20% were not eligible for multivariable modeling. Model coefficients, AUC, calibration slope, and classification metrics from the imputed analysis were compared with complete-case results.

Software and reproducible reporting

All analyses were performed using R version 4.3.2 and RStudio version 2023.12.1. Logistic regression was performed using the base stats package. ROC analysis and DeLong comparisons were performed using pROC version 1.18.5. Calibration analysis was performed using rms version 6.7-1 and ResourceSelection version 0.3-6. Bootstrap validation used 1,000 resamples with a fixed random seed of 70558. Decision curve analysis was performed using rmda version 1.6. Multiple imputation, if required, was performed using mice version 3.16.0. The analysis script reproduced the participant flow, model coefficients, ROC curves, calibration metrics, bootstrap validation, decision curve analysis, cutoff-based analyses, and statistical tables.

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