ArticleFrontiers in oncology2022
Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessment of GALAD score as a diagnostic and prognostic factor ofHepatology forum · 2026Article
- Protein Induced by Vitamin K Absence or Antagonist II in Primary Liver Cancer: Basic Research Insights and Clinical Applications.Journal of clinical and translational hepatology · 2025Review
- [Diagnosis and treatment of liver diseases in the era of artificial intelligence].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Chinese expert consensus statement on the clinical application of AFP/AFP-L3%/DCP using GALAD and GALAD-like algorithm in HCC.Journal of clinical laboratory analysis · 2023Article
- A Statistical Approach to the Diagnosis and Prediction of HCC Using CK19 and Glypican 3 Biomarkers.Diagnostics (Basel, Switzerland) · 2023Article
- Assessing the Predictive Accuracy of the aMAP Risk Score for Hepatocellular Carcinoma (HCC): Diagnostic Test Accuracy and Meta-analysis.Journal of clinical and experimental hepatologyReview
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: GALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations. Methods: There are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model. Result: The GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888). Conclusions: The new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed.
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