Artificial intelligence algorithm was used to establish and verify the prediction model of portal hypertension in hepatocellular carcinoma based on clinical parameters and imaging features.

ABSTRACT:

Background

Portal hypertension (PHT) is an important factor leading to a poor prognosis in patients with hepatocellular carcinoma (HCC). Identifying patients with PHT for individualized treatment is of great clinical significance. The prediction model of HCC combined PHT is in urgent need of clinical practice. Combining clinical parameters and imaging features can improve prediction accuracy. The application of artificial intelligence algorithms can further tap the potential of data, optimize the prediction model, and provide strong support for early intervention and personalized treatment of PHT. This study aimed to establish a prediction model of PHT based on the clinicopathological features of PHT and computed tomography scanning features of the non-tumor liver area in the porta

SUBMITTER: He Y 

PROVIDER: S-EPMC11921233 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature