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.
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.
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).
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.