Extrahepatic cholangiocarcinoma (CCA) is a rare and aggressive type of cancer, presenting as a mass or as a biliary stricture. This review summarizes the utility of endoscopic ultrasound (EUS) in the detection, staging, and determination of the differential diagnosis, especially when no cause of bile duct dilatation is revealed by cross-sectional imaging. The EUS detection rate for distal CCAs is higher than that for the proximal CCAs. The accuracy of T staging varies between 60 and 80%, and vascular involvement is correctly assessed by conventional EUS. EUS-tissue acquisition from the primary tumors is reserved for unresectable or metastatic CCA, especially in distal strictures or mass CCAs. For proximal lesions, EUS could be performed as an adjunctive to ERCP sampling when the latter is inconclusive. EUS is not appropriate for assessing the malignant features of lymph nodes in CCAs. Lymph node EUS-tissue acquisition should be performed only if it changes the surgical decision. Perhaps the development of EUS-fine needle biopsy and the detection of molecular genetic alteration will increase the diagnostic yield in CCAs.

Background: Myocardial scarring is a primary pathogenetic process in nonischemic dilated cardiomyopathy (NIDCM) that is responsible for progressive cardiac remodeling and heart failure, severely impacting the survival of these patients. Although several collagen turnover biomarkers have been associated with myocardial fibrosis, their clinical utility is still limited. Late gadolinium enhancement (LGE) determined by cardiac magnetic resonance imaging (CMR) has become a feasible method to detect myocardial replacement fibrosis. We sought to evaluate the association between collagen turnover biomarkers and replacement myocardial scarring by CMR and, also, to test their ability to predict outcome in conjunction with LGE in patients with NIDCM. Method: We conducted a prospective study on 194 patients (48.7 ± 14.3 years of age; 74% male gender) with NIDCM. The inclusion criteria were similar to those for the definition of NIDCM, performed exclusively by CMR: (1) LV dilation with an LV end-diastolic volume (LVEDV) of over 97 mL/m²; (2) global LV dysfunction, expressed as a decreased LVEF of under 45%. CMR was used to determine the presence and extent of LGE. Several collagen turnover biomarkers were determined at diagnosis, comprising galectin-3 (Gal3), procollagen type I carboxy-terminal pro-peptide (PICP) and N-terminal pro-peptide of procollagen type III (PIIINP). A composite outcome (all-cause mortality, ventricular tachyarrhythmias, heart failure hospitalization) was ascertained over a median of 26 months. Results: Gal3, PICP and PIIINP were considerably increased in those with LGE+ (p < 0.001), also being directly correlated with LGE mass (r² = 0.42; r² = 0.44; r² = 0.31; all p < 0.001). Receiver operating characteristic (ROC) analysis revealed a significant ability to diagnose LGE, with an area under the ROC of 0.816 for Gal3, 0.705 for PICP, and 0.757 for PIIINP (all p < 0.0001). Kaplan–Meier analysis showed that at a threshold of >13.8 ng/dL for Gal3 and >97 ng/dL for PICP, they were able to significantly predict outcome (HR = 2.66, p < 0.001; HR = 1.93, p < 0.002). Of all patients, 17% (n = 33) reached the outcome. In multivariate analysis, after adjustment for covariates, only LGE+ and Gal3+ remained independent predictors for outcome (p = 0.008; p = 0.04). Nonetheless, collagen turnover biomarkers were closely related to HF severity, providing incremental predictive value for severely decreased LVEF of under 30% in patients with NIDCM, beyond that with LGE alone. Conclusions: In patients with NIDCM, circulating collagen turnover biomarkers such as Gal3, PICP and PIIINP are closely related to the presence and extent of LGE and can significantly predict cardiovascular outcome. The joint use of LGE with Gal3 and PICP significantly improved outcome prediction.

In this single-center cross-sectional study on patients undergoing coronary computed tomography angiography (CCTA), we assessed the prognostic significance of metabolic dysfunction associated steatotic liver disease (MASLD), metabolic syndrome (MetS), and CCTA-derived parameters for predicting major adverse cardiovascular events (MACE). Over a mean follow-up of 26.9 months, 2038 patients were analyzed, with 361 (17.7%) experiencing MACE. MASLD was associated with a higher MACE incidence (25.90% vs. 14.71% without MASLD, p < 0.001). Cox regression revealed significant associations between MASLD, coronary calcium score (CCS), number of plaques (NoP), epicardial fat volume (EFV), and MACE, with hazard ratios of 1.843, 1.001, 1.097, and 1.035, respectively (p < 0.001 for all). A composite risk score integrating CCS, NoP, EFV, and MASLD demonstrated superior predictive value for MACE (AUC = 0.948) compared to individual variables (p < 0.0001 for all). In conclusion, MASLD is linked to an elevated risk of MACE, and a comprehensive risk-scoring system incorporating imaging and clinical factors enhances MACE prediction accuracy.

Introduction: Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques. Objective: This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct. Materials and Methods: In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model’s robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca. Results: The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation. Conclusions: This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.

Background: Cholangiocarcinoma (CCA) is a highly aggressive cancer of the bile ducts with a poor prognosis and limited diagnostic markers. This study aims to investigate the potential of miR-18a and miR-532 as biomarkers for CCA by exploring their correlations with clinical parameters and traditional tumor markers such as CA19.9, CEA, and AFP. Methods: This study involved a cohort of patients diagnosed with CCA. Serum levels of miR-18a and miR-532 were measured and analyzed in relation to various clinical parameters, including age, tumor markers, and histological features. Results: Serum levels of miR-18a and miR-532 were upregulated in patients with extrahepatic cholangiocarcinoma (eCCA) compared to healthy controls (p < 0.05). MiR-18a and miR-532 levels were correlated with each other (p = 0.011, Spearman’s rho = 0.482) but showed no significant correlation with age or traditional tumor markers (CA19.9, CEA, AFP). No significant differences in miR-18a and miR-532 levels were observed concerning tumor localization or histological grading. For predicting tumor resectability, miR-532 at a cut-off point of 2.12 showed a sensitivity of 72.73%, specificity of 81.25%, and an AUC of 71.3%, while miR-18a, at a cut-off of 1.83, had a sensitivity of 63.64%, specificity of 75%, and an AUC of 59.7%. ROC curve analysis suggested moderate diagnostic potential for miR-18a and miR-532, with AUC values of 0.64 and 0.689, respectively. Conclusions: Although miR-18a and miR-532 showed significant upregulation in eCCA patients compared to healthy controls, they did not demonstrate significant associations with key clinical parameters, limiting their effectiveness as standalone diagnostic biomarkers. Further research involving larger, multi-center cohorts and additional molecular markers is necessary to validate these findings and explore the broader diagnostic potential of miRNAs in CCA.

Background: Aortic valve stenosis (AS) is the most common degenerative valvular heart disease, significantly impacting the outcome. Current guidelines recommend valve replacement only for symptomatic patients, but advanced cardiovascular imaging, particularly cardiac magnetic resonance (CMR), may refine these recommendations. Feature-tracking CMR (FT-CMR) effectively assesses left ventricular (LV) strain and shows promise in predicting major adverse cardiovascular events (MACEs), though data on AS are limited. This study explored the role of CMR-derived LV strain in predicting MACEs occurrence in patients with severe AS. Method: We prospectively assessed 84 patients with severe AS and 84 matched controls. Global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) were evaluated using FT-CMR. A composite endpoint—cardiac death, ventricular tachyarrhythmias, and heart failure hospitalization—was analyzed over a median follow-up of 31 months. Results: GLS was considerably reduced in AS patients (−15.8% vs. −19.7%, p < 0.001) and significantly predicted MACEs (HR = 1.24, p = 0.002) after adjusting for LVEF, 6 min walk distance, native T1, and late gadolinium enhancement. This underscores GLS’s unique and robust predictive capability for MACEs in severe AS patients. Kaplan–Meier curves and ROC analysis both showed that GLS had the highest predictive performance for MACEs, with an AUC of 0.857. Conclusions: GLS provided independent incremental predictive value for outcome.