Cystic echinococcosis (CE), caused by the metacestode of Echinococcus granulosus sensu lato, is a zoonosis that remains endemic across the Middle East, the Mediterranean basin, North Africa and South America, and the liver is the organ most frequently involved. Because serological assays are of limited sensitivity and specificity, imaging carries the diagnostic burden: ultrasonography (US) is the cornerstone for initial diagnosis, staging, treatment planning and longitudinal follow-up; computed tomography (CT) best defines anatomical location, vascular relationships, mural calcification and acute complications such as rupture and superinfection; and magnetic resonance imaging (MRI) more faithfully reproduces US-defined staging morphology than CT and provides superior soft-tissue contrast for assessing biliary involvement and separating CE cysts from cystic neoplasms. The aim of this pictorial review is fourfold: to illustrate the US, CT and MRI appearance of each World Health Organization Informal Working Group on Echinococcosis (WHO-IWGE) stage of hepatic CE and to explain how stage assignment translates directly into a stage-specific treatment decision—anti-parasitic treatment, percutaneous treatment, surgery or watch-and-wait; to present, stage by stage, the hepatic lesions that mimic CE and the findings that discriminate them; to depict the wall signs of the CE cyst that become visible with high-frequency linear transducers, including the three-layered wall, adherent brood capsules and hydatid sand; and to review the imaging of complicated CE together with the expected post-treatment appearance, procedure-related complications and the imaging strategy for detecting recurrence.
Advanced fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) involves metabolic and anatomic remodeling beyond the liver. We developed and internally validated an interpretable CT-based score integrating abdominal body-composition and organ measurements and compared it with the Fibrosis-4 (FIB-4) index. We retrospectively studied 151 patients with biopsy-confirmed MASLD (86 advanced [F3–F4], 65 early-stage [F0–F2] fibrosis). Deep learning-based segmentation quantified abdominal adipose tissue, skeletal muscle, bone, liver, and spleen measurements. The CT-Fibrosis Score was developed using repeated (50×) stratified 5-fold cross-validation, with imputation, standardization, and feature selection confined to each training fold and inner 5-fold cross-validation for hyperparameter selection. Performance was based on out-of-fold predictions and compared with FIB-4 using the DeLong test. Sensitivity analyses addressed missing data, age dependence, and ascites. Four features were consistently selected: age, bone-to-abdominal-cavity volume ratio, visceral adipose tissue (VAT) density, and spleen index. The CT-Fibrosis Score achieved an out-of-fold AUC of 0.77 (95
To investigate the feasibility of automatic training data generation using virtual non-contrast (VNC) images of dual-energy CT and to evaluate the performance of medical segmentation models trained on auto-generated data. Training images were generated from contrast-enhanced CT (CECT) scans using VNC algorithm. Corresponding training labels were generated from iodine-density images via intensity thresholding. The paired VNC images and labels constituted the dataset (791 cases in total) for training non-enhanced CT (NECT) segmentation models. We attempted to train segmentation models with VNC images alone, as well as the combination of VNC and real NECT images. Models trained with auto-generated data effectively performed renal vein segmentation on NECT images, achieving Dice score of 69.60
This systematic review and meta-analysis evaluated the diagnostic performance of FDG PET/MR for detecting hepatic metastases from extrahepatic primary malignancies, together with acquisition protocols, reconstruction parameters, and technical heterogeneity. A systematic search was conducted through September 2026. Eligible studies evaluated FDG PET/MR for hepatic metastases and reported diagnostic performance data and/or technical protocol information. Summary estimates of sensitivity and specificity were obtained using a bivariate random-effects model. A structured qualitative assessment of PET and MR acquisition, reconstruction parameters, and image interpretation strategies was also performed. Fifteen studies involving 861 patients met the inclusion criteria. Eight studies (53
