Early identification of hepatocellular carcinoma (HCC) in individuals with chronic hepatitis B virus (HBV) infection is vital for improving patient outcomes. A recent study at the Third Hospital of Hebei Medical University has introduced the DSGAA model, which utilizes abnormal levels of des-gamma-carboxy prothrombin (DCP) to advance early-stage diagnosis of HBV-related HCC.
Innovative Approach in Diagnostic Modeling
The research retrospectively analyzed 420 cases with chronic HBV infection and nodular liver lesions observed through imaging from January 2021 to June 2024. Participants were categorized into the HBV-HCC group, consisting of 182 cases, and a control group with 238 cases based on current diagnostic criteria for HCC. The study evaluated patient demographics, liver biochemical markers, serum DCP, alpha-fetoprotein (AFP) levels, and the effectiveness of combined detection methods for early HCC diagnosis. The DSGAA model was constructed by integrating DCP with gender, I3-glutamyl transferase (GGT), AFP, and age as independent variables, and its diagnostic performance was benchmarked against traditional models using nomogram visualization and calibration curves.
- DCP demonstrated a significantly higher detection rate for HBV-HCC compared to AFP.
- The DSGAA model achieved an area under the ROC curve of 0.8841, indicating strong diagnostic ability.
- Early-stage HCC diagnosis showed improved specificity with the DSGAA model over traditional methods.
Significant Improvements in Diagnostic Accuracy
Findings revealed that patients with HCC exhibited higher levels of age, sex-related factors, hemoglobin, albumin, alanine aminotransferase, alkaline phosphatase, and GGT compared to the control group. The DCP marker alone achieved an 85.71% positivity rate in HBV-HCC patients, outperforming AFP’s 59.89%. Notably, among AFP-negative patients, the abnormal detection rate of DCP remained high at 76.7%. The DSGAA model further enhanced diagnostic sensitivity to 80.22% and specificity to 86.13%, surpassing traditional models in both overall and early-stage HCC detection.
The study underscores the potential of the DSGAA model in clinical settings, offering a more reliable tool for the early detection of HCC in HBV-infected patients. By integrating multiple biomarkers and patient-specific factors, the model provides a comprehensive approach that could lead to timely interventions and better patient management.
Implementing the DSGAA model in routine screenings could significantly impact the landscape of HCC diagnosis, particularly in high-risk populations. Healthcare providers may benefit from adopting this model to enhance diagnostic accuracy and improve prognostic outcomes for patients with chronic HBV infection.
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