Development and validation of a high-confidence diagnostic model integrating ctDNA methylation and serum biomarkers for early-stage hepatocellular carcinoma detection

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with significantly improved prognosis when detected early in high-risk populations. Current serum biomarkers show limited sensitivity for early-stage disease. We developed and validated GAMAD, a multimodal diagnostic model integrating circulating tumor DNA (ctDNA) methylation with established serum markers to enhance early HCC detection in China. In this multicenter prospective trial, a total of 1,692 patients were enrolled: 476 with HCC, 645 with hepatitis, 443 with cirrhosis, and 128 with no detectable liver abnormalities. Blood tests including AFP, AFP-L3, DCP, and ctDNA methylation (HepaAiQ) were performed. Using training, validation, and independent test cohorts, we developed the GAMAD model by integrating HepaAiQ with gender, age, AFP, and DCP. HepaAiQ demonstrated a superior performance compared with AFP, DCP, and AFP-L3 with a sensitivity of 74.6%, specificity of 88.1%, and an area under the curve (AUC) of 0.862 (95% CI, 0.842–0.883) in all samples. The GAMAD model, optimized specifically for early-stage HCC, achieved sensitivity of 80.5%, specificity of 90.4%, and AUC of 0.934 (95% CI, 0.911–0.957) in the validation cohort. In the independent test cohort, GAMAD showed superior performance with sensitivity of 86.5% for stage 0/A and 91.7% for stage B-C HCC (AUC 0.952, 95% CI, 0.931–0.973), substantially outperforming the GALAD model across all cohorts. The GAMAD model represents a significant clinical advancement for early HCC detection. Its intended clinical use is to serve as an adjunct to standard imaging for high-risk populations, substantially increasing early diagnosis rates.