Cholangiocarcinoma (CCA) presents significant diagnostic challenges, resulting in late patient diagnosis and poor survival rates. Primary Sclerosing Cholangitis (PSC) patients pose a particularly difficult clinical dilemma, since they harbor chronic biliary strictures that are difficult to distinguish from CCA. MicroRNAs (miRs) have recently emerged as a valuable class of diagnostic markers; however, thus far, neither extracellular vesicles (EVs) nor miRs within EVs have been investigated in human bile. We aimed to comprehensively characterize human biliary EVs, including their miR content. Conclusion We have established the presence of extracellular vesicles in human bile. In addition, we have demonstrated that human biliary EVs contain abundant miR species, which are stable and therefore amenable to the development of disease marker panels. Furthermore, we have characterized the protein content, size, numbers and size distribution of human biliary EVs. Utilizing Multivariate Organization of Combinatorial Alterations (MOCA), we defined a novel biliary vesicle miR-based panel for CCA diagnosis which demonstrated a sensitivity of 67% and specificity of 96%. Importantly, our control group contained 13 PSC patients, 16 patients with biliary obstruction of varying etiologies (including benign biliary stricture, papillary stenosis, choledocholithiasis, extrinsic compression from pancreatic cysts, and cholangitis), and 3 patients with bile leak syndromes. Clinically, these types of patients present with a biliary obstructive clinical picture that could be confused with CCA. These findings establish the importance of using extracellular vesicles, rather than whole bile, for developing miR-based disease markers in bile. Finally, we report the development of a novel bile-based CCA diagnostic panel that is stable, reproducible, and has potential clinical utility.
Objectives-To develop and validate a proof-of-concept convolutional neural network (CNN)based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.Methods-A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.
Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques.
Objectives-To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier. Methods-A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification.
Purpose: Embolotherapy using microshperes is currently performed with soluble contrast to aid in visualization. However, administered payload visibility dimishes soon after delivery due to soluble contrast washout, leaving the radiolucent bead's location unknown. The objective of our study was to characterize inherently radiopaque beads (RO Beads) in terms of physicomechanical properties, deliverability and imaging visibility in a rabbit VX2 liver tumor model.Materials and Methods: RO Beads, which are based on LC Bead® platform, were compared to LC Bead. Bead size (light microscopy), equilibrium water content (EWC), density, X-ray attenuation and iodine distribution (micro-CT), suspension (settling times), deliverability and in vitro penetration were investigated. Fifteen rabbits were embolized with either LC Bead or RO Beads + soluble contrast (iodixanol-320), or RO Beads+dextrose. Appearance was evaluated with fluoroscopy, X-ray single shot, cone-beam CT (CBCT).Results: Both bead types had a similar size distribution. RO Beads had lower EWC (60-72%) and higher density (1.21-1.36 g/cc) with a homogeneous iodine distribution within the bead's interior. RO Beads suspension time was shorter than LC Bead, with durable suspension (>5 min) in 100% iodixanol. RO Beads ≤300 µm were deliverable through a 2.3-Fr microcatheter. Both bead types showed similar penetration. Soluble contrast could identify target and non-target embolization on fluoroscopy during administration. However, the imaging appearance vanished quickly for LC Bead as contrast washed-out. RO Beads+contrast significantly increased visibility on X-ray single shot compared to LC Bead+contrast in target and non-target arteries (P=0.0043). Similarly, RO beads demonstrated better visibility on CBCT in target arteries (P=0.0238) with a trend in non-target arteries (P=0.0519). RO Beads+dextrose were not sufficiently visible to monitor embolization using fluoroscopy.Conclusion: RO Beads provide better conspicuity to determine target and non-target embolization compared to LC Bead which may improve intra-procedural monitoring and post-procedural evaluation of transarterial embolization.
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