# Validation of the GALAD model for diagnosing HBV-related hepatocellular carcinoma in Chinese patients

## TL;DR

The GALAD model is highly accurate and cost-effective for diagnosing HBV-related liver cancer in Chinese patients, outperforming traditional biomarkers.

## Contribution

Validation of the GALAD model's effectiveness in diagnosing HBV-related hepatocellular carcinoma in a Chinese population.

## Key findings

- GALAD achieved an AUC of 0.942, with 89.91% sensitivity and 81.51% specificity at an optimal cutoff.
- The model showed excellent calibration (χ2 = 8.934, p > 0.05), confirming its reliability for Chinese HBV-related HCC patients.

## Abstract

•GALAD model shows high accuracy in diagnosing HBV-related HCC in Chinese patients.•GALAD outperforms AFP, AFP-L3 %, and DCP in diagnostic performance.•GALAD is a cost-effective tool for HCC screening in HBV-endemic and in resource-limited regions.

GALAD model shows high accuracy in diagnosing HBV-related HCC in Chinese patients.

GALAD outperforms AFP, AFP-L3 %, and DCP in diagnostic performance.

GALAD is a cost-effective tool for HCC screening in HBV-endemic and in resource-limited regions.

The GALAD model integrates serological biomarkers (AFP, AFP-L3%, DCP) with demographic factors to estimate the presence of Hepatocellular Carcinoma (HCC). However, its applicability in Chinese populations with HBV-related HCC remains underexplored. This study validated the clinical utility of GALAD in this specific population.

A retrospective cohort study enrolled 217 HBV-related HCC patients, 210 patients with benign Liver disease without cirrhosis, 247 Cirrhosis patients, and 220 healthy controls. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration.

GALAD demonstrated superior diagnostic performance, with an Area Under the Curve (AUC) of 0.942. At an optimal cutoff of 1.89, sensitivity and specificity were 89.91 % and 81.51 %, respectively. The model showed excellent calibration (χ2 = 8.934, p > 0.05), confirming its reliability for Chinese HBV-related HCC patients.

The GALAD model outperforms individual biomarkers in diagnosing HBV-related HCC in the Chinese population, highlighting its potential utility in clinical practice. This cost-effective tool holds particular promise for the tertiary prevention of HBV-related HCC, especially in resource-limited settings.