CT imaging features and diagnostic algorithm for hepatic cystic echinococcosis

Abstract

Abstract To systematically analyze CT imaging features of hepatic cystic echinococcosis (CE), explore radiological-pathological correlations, and develop a diagnostic algorithm for accurate disease classification. This retrospective study included 48 pathologically confirmed cases of hepatic CE from two medical centers. CT imaging features were analyzed by two experienced radiologists, evaluating lesion characteristics including location, morphology, wall features, and calcification patterns. Imaging findings were correlated with pathological results. A diagnostic algorithm was developed and validated, with inter-observer agreement assessed using Fleiss kappa coefficient. Seven distinct CT imaging patterns were identified, corresponding to different pathological stages: unilocular cystic (25.0%), multivesicular (8.3%), collapsed inner wall (10.4%), partially solidified (10.4%), solidified (16.7%), and calcified (25.0%) types, with complicated cases (4.2%) showing additional features. The proposed diagnostic algorithm achieved 94.0% accuracy (451/480 classifications) in validation testing by ten junior radiologists, with excellent inter-observer agreement (quadratic-weighted Fleiss kappa coefficient = 0.740 [95% CI 0.577–0.902], Gwet’s AC2 coefficient = 0.768). Primary diagnostic challenges involved differentiating between CE2 and CE3b lesions, and between CE3b and CE4 lesions. This study explores the correlation between CT imaging patterns and pathological stages of hepatic CE, proposing a validated diagnostic algorithm. The findings provide valuable insights for CE classification, particularly in regions where the disease is emerging or underrecognized.

Keywords

• Cystic echinococcosis
• Computed tomography
• Pathology
• Diagnostic algorithm
• Liver diseases
• Parasitic