---
title: Diagnostic Accuracy of APAC, ASAP and GALAD Scores in Hepatocellular Carcinoma Among Cirrhotic Patients
nct_id: NCT05738772
overall_status: UNKNOWN
sponsor: Sohag University
study_type: OBSERVATIONAL
primary_condition: Hepatocellular Carcinoma
countries: Egypt
canonical_url: "https://parkinsonspathways.com/agent/trials/NCT05738772.md"
clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT05738772"
ct_last_update_post_date: 2023-02-22
last_seen_at: "2026-05-12T07:12:48.285Z"
source: ClinicalTrials.gov (mirrored, no enrichment)
---
# Diagnostic Accuracy of APAC, ASAP and GALAD Scores in Hepatocellular Carcinoma Among Cirrhotic Patients

**NCT ID:** [NCT05738772](https://clinicaltrials.gov/study/NCT05738772)

## Key Facts

- **Status:** UNKNOWN
- **Study Type:** OBSERVATIONAL
- **Target Enrollment:** 90
- **Lead Sponsor:** Sohag University
- **Conditions:** Hepatocellular Carcinoma
- **Start Date:** 2023-02-15
- **Completion Date:** 2024-03
- **CT.gov Last Update:** 2023-02-22

## Brief Summary

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with most patients developing HCC due to chronic liver diseases. Unfortunately, HCC has a morality to incidence ratio that approaches 1.

Among the etiological factors associated with HCC, hepatitis C virus (HCV) and Hepatitis B virus (HBV) infections are major risk factors. Despite HBV vaccination programs and effective direct antiviral agents (DAA) for treatment of HCV, the incidence of virus-related HCC remains high. HCV eradication by antiviral treatment reduces but does not eliminate HCC risk. Patients with HCV-related cirrhosis require HCC surveillance even after sustained virologic response (SVR) due to a persistent risk of HCC even years after SVR . In Egypt, HCC represents the fourth common cancer and is the most common cause of mortality-related and morbidity-related cancer. Egypt ranks the third and 15th most populous country in Africa and worldwide, respectively, and the Egyptian health authorities consider HCC as one of the most challenging health problems for the current decade. Both HCC screening and monitoring efforts have improved significantly since 2018 as a result of the national screening campaign .The early diagnosis of HCC is essential to initiate curative treatments to improve short term and long-term prognosis. Therefore, highly effective methods are needed to detect HCC at an earlier stage. American Association for the Study of Liver Diseases (AASLD) and European Association for the Study of the Liver (EASL) guidelines recommend the periodic use of ultrasound scanning (USS), with or without Alpha-fetoprotein (AFP) evaluation, for HCC surveillance. However, suboptimal performance of USS has been reported, with its sensitivity being compromised by the extent of liver cirrhosis, high body mass index (BMI), etiology of liver disease, expertise of the operator and quality of the equipment. Moreover, its sensitivity and specificity for early-stage HCC was found to be rather low . Serum biomarkers play an essential role in diagnosing HCC, as biomarkers are often more convenient, inexpensive, non-invasive, and reproducible . Alpha-fetoprotein (AFP) is a widely used biomarker for HCC diagnosis. The diagnostic accuracy of AFP is limited, however, due to its high false-negative rate to detect small or early stage tumors. As previous studies have demonstrated, the sensitivity of AFP among patients with HCC was 52% for tumors \> 3cm and dropped to only 25% for tumors \< 3cm. In addition, AFP may also be elevated in some benign liver diseases, such as chronic hepatitis and cirrhosis even in the absence of HCC.

## Eligibility

- **Minimum age:** 18 Years
- **Sex:** ALL
- **Healthy Volunteers:** No

```
Inclusion Criteria:

* A total of 90 adult patients with liver cirrhosis attending the outpatient clinic or inpatient section of the department of tropical medicine and gastroenterology at Sohag University Hospital will be included in the study. Patients will be divided into two groups

Exclusion Criteria:

* 1\. patients aged \<18 years old. 2. Presence of clinically suspected other causes of hepatocellular injury (any history of alcoholism, autoimmune hepatitis, primary sclerosing cholangitis (PSC), primary biliary cholangitis (PBC), Wilson's disease, fatty liver diseases with metabolic syndrome \& drug induced liver disease). 3. Receipt of any tumor specific therapy before blood samples collection. 4. Any patients who are on warfarin will be excluded as warfarin can elevate the DCP level in the absence of HCC. 5. Patients having malignancies other than HCC. 6. Presence of distant metastasis. 7. Presence of venous thromboembolism including portal vein thrombosis
```

## Arms

- **hcc group** — The study group will include 45 patients with HCC on top of liver cirrhosis. All virus-related liver cirrhosis and all BCLC stages of HCC will be accepted. Verified presence of HCC, will be assessed by computed tomography (CT) and/or magnetic resonance imaging (MRI) or based on histological validation. In patients with presence of liver cirrhosis, non-invasive diagnosis of HCC is standard, when dynamic imaging shows typical diagnostic patterns as the combination of hypervascularity in late arterial phase and washout on portal venous and/or delayed phases
- **LC group** — Will include 45 patients diagnosed with liver cirrhosis on top of HCV or HBV with an absence of focal lesions on ultrasound screening as a control group. Cirrhosis will be determined according to clinical, serological, and radiological findings

## Interventions

- **serum sample for ELIZA** (DIAGNOSTIC_TEST) — Assay of AFP, AFP-L3, DCP and Soluble PDGFRβwill be done by enzyme-linked immunosorbent assay (ELISA).

## Primary Outcomes

- **serum level of AFP-L3.** _(time frame: one year)_ — Assay of AFP-L3 will be done by enzyme-linked immunosorbent assay (ELISA) then Diagnostic scoring tools were calculated using the following formulae:The GALAD score will be calculated using the following equation: GALAD score = - 10.08 + 0.09 × age + 1.67 × gender + 2.34 × Lg (AFP \[ng/ml\]) + 0.04 × AFP-L3%% + 1.33 × Lg (PIVKA-II \[mAU/ml\]), where gender = 0 for females and 1 for males. - The probability of HCC in a patient was calculated as follows: Pr (HCC)=exp (Z)/ (1 + exp (Z)) (z: GALAD)The ASAP score was calculated using the following equation: ASAP score = -7.58 + 0.05

× age - 0.58 × gender +0.42 × Ln (AFP \[ng/ml\]) + 1.11 × Ln (PIVIKA-II \[mAU/ml\]), where gender = 0 for males and 1 for femalesThe APAC score = (Age \[years\] x 0.20480) - (log10(sPDGFRβ \[pg/mL\]) x 1.98684) + (log10(AFP \[ng/mL\]) x 2.45657) - (Creatinine \[mg/dL\] x 2.46891) - 4.36493
- **serum level of DCP** _(time frame: 1 year)_ — Assay of DCP will be done by enzyme-linked immunosorbent assay (ELISA) then Diagnostic scoring tools were calculated using the following formulae:The GALAD score will be calculated using the following equation: GALAD score = - 10.08 + 0.09 × age + 1.67 × gender + 2.34 × Lg (AFP \[ng/ml\]) + 0.04 × AFP-L3%% + 1.33 × Lg (PIVKA-II \[mAU/ml\]), where gender = 0 for females and 1 for males. - The probability of HCC in a patient was calculated as follows: Pr (HCC)=exp (Z)/ (1 + exp (Z)) (z: GALAD)The ASAP score was calculated using the following equation: ASAP score = -7.58 + 0.05

× age - 0.58 × gender +0.42 × Ln (AFP \[ng/ml\]) + 1.11 × Ln (PIVIKA-II \[mAU/ml\]), where gender = 0 for males and 1 for femalesThe APAC score = (Age \[years\] x 0.20480) - (log10(sPDGFRβ \[pg/mL\]) x 1.98684) + (log10(AFP \[ng/mL\]) x 2.45657) - (Creatinine \[mg/dL\] x 2.46891) - 4.36493
- **Serum level of Soluble PDGFRβ** _(time frame: 1 year)_ — Assay of Soluble PDGFRβ will be done by enzyme-linked immunosorbent assay (ELISA) then Diagnostic scoring tools were calculated using the following formulae:The GALAD score will be calculated using the following equation: GALAD score = - 10.08 + 0.09 × age + 1.67 × gender + 2.34 × Lg (AFP \[ng/ml\]) + 0.04 × AFP-L3%% + 1.33 × Lg (PIVKA-II \[mAU/ml\]), where gender = 0 for females and 1 for males. - The probability of HCC in a patient was calculated as follows: Pr (HCC)=exp (Z)/ (1 + exp (Z)) (z: GALAD)The ASAP score was calculated using the following equation: ASAP score = -7.58 + 0.05

× age - 0.58 × gender +0.42 × Ln (AFP \[ng/ml\]) + 1.11 × Ln (PIVIKA-II \[mAU/ml\]), where gender = 0 for males and 1 for femalesThe APAC score = (Age \[years\] x 0.20480) - (log10(sPDGFRβ \[pg/mL\]) x 1.98684) + (log10(AFP \[ng/mL\]) x 2.45657) - (Creatinine \[mg/dL\] x 2.46891) - 4.36493

## Locations (1)

- Sohag University Hospital, Sohag, Egypt

## Recent Field Changes (last 30 days)

- `status.overallStatus` — added _(2026-05-12)_
- `status.primaryCompletionDate` — added _(2026-05-12)_
- `status.completionDate` — added _(2026-05-12)_
- `status.lastUpdatePostDate` — added _(2026-05-12)_
- `design.enrollmentCount` — added _(2026-05-12)_
- `eligibility.criteria` — added _(2026-05-12)_
- `eligibility.minAge` — added _(2026-05-12)_
- `eligibility.sex` — added _(2026-05-12)_
- `outcomes.primary` — added _(2026-05-12)_
- `armsInterventions.arms` — added _(2026-05-12)_
- `armsInterventions.interventions` — added _(2026-05-12)_
- `sponsor.lead` — added _(2026-05-12)_
- `results.hasResults` — added _(2026-05-12)_
- `locations.sohag university hospital|sohag||egypt` — added _(2026-05-12)_

---

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