ArticleScientific reports2025
CT imaging features and diagnostic algorithm for hepatic cystic echinococcosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and External Validation of an Efficient Deep Learning Model for Lesion Segmentation and Subtyping of Hepatic Echinococcosis in Ultrasound Imaging.Health care science · 2026Article
- Magnetic Resonance Imaging Features of Hepatic Hydatid Disease: A Pictorial Review with Emphasis on Atypical Presentations and Differential Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Article
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7 authors.
Funding
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.
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