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Clinical Utility of the aMAP Score for Predicting Hepatocellular Carcinoma Development in Patients with Chronic Hepatitis B.

Supakorn ChaiwiriyawongSuraphon AssawasuwannakitPoorikorn FeuangwattanaPimsiri SripongpunNaichaya ChamroonkulTeerha PiratvisuthApichat Kaewdech
Published in: Diagnostics (Basel, Switzerland) (2024)
This study aimed to evaluate the efficacy of the aMAP score and compare it with other risk scores for predicting hepatocellular carcinoma (HCC) development in Thai patients with chronic hepatitis B (CHB). We retrospectively analyzed patients with CHB between 1 January 2008 and 31 December 2019. Data on demographics, clinical parameters, cirrhosis status, HCC imaging, and alpha fetoprotein surveillance were collected to calculate the aMAP score (0-100) based on age, sex, albumin-bilirubin level, and platelet count. Of the 1060 patients analyzed, 789 were eligible, of whom 51 developed HCC. The cumulative HCC incidences in the low-, moderate-, and high-risk groups at 3, 5, and 10 years were significantly different (log-rank, p < 0.0001). The area under the receiver operating characteristic curves (AUROCs) of the aMAP scores for predicting HCC at 3, 5, and 10 years were 0.748, 0.777, and 0.784, respectively. Among the risk scores, the CU-HCC score had the highest AUROCs (0.823) for predicting 5-year HCC development. The aMAP score is a valuable tool for predicting HCC risk in Thai patients with CHB and can enhance surveillance strategies. However, its performance is inferior to that of the CU-HCC score, suggesting the need for new predictive tools for HCC surveillance.
Keyphrases
  • public health
  • end stage renal disease
  • high resolution
  • newly diagnosed
  • chronic kidney disease
  • machine learning
  • prognostic factors
  • peritoneal dialysis
  • deep learning