Objective: To compare on-site and blinded off-site reading of baseline ultrasound (US) and contrast enhanced ultrasound (CEUS) for classification and characterisation of focal liver lesions.

Materials And Methods: 99 patients (57 women and 42 men, age range 18-89 years, mean age: 59 years) with 53 malignant and 46 benign liver lesions were studied with unenhanced US followed by contrast enhanced US after injection of 2.4 ml SonoVue® (Bracco, Milano, Italy). Image interpretation was performed on-site with clinical information available by consensus of two readers and off-site by two independent blinded readers at two different centers. Comparison of pre and post contrast scans and of the different readers was performed. Reference examinations were histology, intraoperative US, MRI or CT.

Results: Sensitivity for malignancy improved from 81/89/66% (on-site/off-site reader 1/2) before to 100/96/96% post contrast administration (p<0.05, except for reader 1). Specificity improved from 48/48/54% on baseline US to 89/80/76% on CEUS (p<0.05). Accuracy for specific lesion diagnosis was 62/59/50% pre and 90/77/72% post contrast (p<0.05). Classification and characterisation post contrast were mildly inferior for off-site reading. Agreement between on-site and off-site readers of unenhanced scans was fair (κ=0.29-0.39) while it was good for CEUS (κ=0.63-0.79).

Conclusions: CEUS improves classification and characterisation of focal liver lesions and interobserver agreement compared to conventional US. Classification and characterisation post contrast were mildly but statistically significantly better for on-site than for off-site reading.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ejrad.2011.10.015DOI Listing

Publication Analysis

Top Keywords

contrast enhanced
12
characterisation focal
8
focal liver
8
liver lesions
8
unenhanced contrast
8
post contrast
8
contrast
5
lesions unenhanced
4
enhanced low
4
low real
4

Similar Publications

Purpose: This case report aims to present a rare case of endometrial carcinosarcoma, a highly malignant tumor with a poor prognosis. The primary objective is to describe this unique case's clinical presentation, multimodal magnetic resonance imaging (MRI) features, typical histopathological characteristics and surgical treatment.

Methods: A detailed analysis of the patient's medical history, preoperative imaging evaluation, and treatment approach was conducted.

View Article and Find Full Text PDF

This study aims to shed light on the mechanism and kinetics of 1,4-dioxane degradation by hydroxyl radical (OH) across various solvation conditions to evaluate electronic and structural properties at the MP2/aug-cc-pVTZ level. Transition states (TS) structures determined in the gas phase and SMD solvation model reveal similar hydrogen abstraction patterns. In contrast, the explicit solvation model (ES) introduces significant changes, suggesting a kinetic preference for axial pathways.

View Article and Find Full Text PDF

: This study aimed to evaluate the diagnostic performance of the Kaiser score (KS) on the modified abbreviated breast magnetic resonance imaging (AB-MRI) protocol for characterizing breast lesions by comparing it with full-protocol MRI (FP-MRI), using the histological data as the reference standard. : Breast MRIs detecting histologically verified contrast-enhancing breast lesions were evaluated retrospectively. A modified AB-MRI protocol was created from the standard FP-MRI, which comprised axial fat-suppressed T2-weighted imaging (T2WI), pre-contrast T1-weighted imaging (T1WI), and first, second, and fourth post-contrast phases.

View Article and Find Full Text PDF

A Comparison Study of Person Identification Using IR Array Sensors and LiDAR.

Sensors (Basel)

January 2025

Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities-RGB, thermal, and depth-using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation.

View Article and Find Full Text PDF

Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!