Congress:
ECR25
Poster Number:
C-11627
Type:
Poster: EPOS Radiologist (scientific)
DOI:
10.26044/ecr2025/C-11627
Authorblock:
Y. Xu, J. Liu, J. Zhou; Lanzhou City/CN
Disclosures:
Yuan Xu:
Nothing to disclose
Jianli Liu:
Nothing to disclose
Junlin Zhou:
Nothing to disclose
Keywords:
Abdomen, Artificial Intelligence, Liver, Conventional radiography, CT-Quantitative, Decision analysis, Surgery, Cancer, Neoplasia
Multivariate logistic regression analysis revealed that kurtosis[5] (P < 0.01), platelet-to-lymphocyte ratio (PLR)[6] (P = 0.03), international normalized ratio (INR)[7] (P < 0.01), albumin-bilirubin (ALBI)[8] grade (P < 0.01), and microvascular invasion (MVI)[7, 9] (P < 0.01) were independent risk factors for ER. The clinical-imaging-histogram (CIH) model demonstrated the best predictive performance for ER (AUC = 0.90), surpassing the clinical model and the imaging-histogram (IH) model (AUCs of 0.86 and 0.71, respectively). The CIH model's calibration curve showed good consistency, and the DCA indicated a high level of clinical utility.