View clinical trials related to Focal Liver Lesions.
Filter by:As a prospective, multi-center study, GUIDER will recruit 400 liver nodules participants from different provinces and regions across China. Except for cfDNA signatures, serum biomarkers, histopathological biopsy and enhanced MRI will also be performed. The sensitivity and specificity of the cfDNA signature based-model in liver nodules diagnosing will be evaluated.
Contrast-enhanced ultrasound (CEUS) substantially improves the potential of ultrasound (US) for the identification and characterization of focal liver lesions (FLLs). Compared to contrasted-enhanced MRI and CT, it has some unique advantages, such as the absence of ionizing radiation, and easy operability and repeatability. However, the efficacy of CEUS in diagnosing liver lesions is challenged by several factors including being highly dependent on doctor's experience, low signal-to-noise ratio, and low interobserver agreement. Therefore, it is a beneficial attempt to construct an intelligent CEUS diagnosis system using digital information technology. This study aims to collect standard data of CEUS cines recordings and develop deep learning model for accurate segmentation, detection and classification of liver lesions.
Ultrasound (US) as first-line imaging technology in detecting focal liver lesions,also plays a crucial role in evaluating image and guiding ablation which is the main treatment for liver lesions. However, the effect of US in diagnosing liver lesions is challenged by several factors including being highly dependent on doctor's experience, low signal-to-noise ratio, low resolution for lesion feature,large error from thermal field evaluation during the process of ablation and so on. Therefore, it is of great significance to construct an intelligent US analysis system depending on the digital information technology. Basing on these problems,the following research will be involved in our project: 1) US database of liver lesions with seamless connection to Picture Archiving and Communication Systems (PACS) will be developed, with the aim to provide standard data for intelligent US analysis. 2) Deep learning model for accurate segmentation, detection and classification of liver lesions on US images will be studied. Then automatic extraction, selection and analysis of liver lesion ultrasound features and the intelligent US diagnosis for liver lesions will be realized. 3) Proposing a clustering model with deep image features, and depicting the similarity measurement of liver cancer, which can be furthered used to link the liver cancer feature to optimal ablation parameters. The intelligent decision-making system for quantifying thermal ablation will be established. 4) Regression algorithm and Generative Adversarial Nets will be developed to extract the image features of liver cancer which will predict risk factors after US-guided thermal ablation.Based on the above researches, it is of great value to establish an intelligent focal liver lesion US diagnosis system involving intelligent diagnosis,personalized ablation strategy and accurate prognosis evaluation, improving the level of accurate diagnosis and treatment of liver lesions.
The purpose of this study is to register the precision of image fusion between live sonography and CT, MRI or PET/CT of liver lesions and other focal lesions in the abdomen. To test whether it will be possible to biopsy guided by image fusion.
The conventional sonography is frequently used to detect incidental focal liver lesions because of its availability, innocuity and low cost. Nevertheless, sensibility and specificity of conventional sonography does not exceed 70% for tumoral affections. Consequently the interest of this practice must be reconsidered by studying its ratio cost/diagnosis contribution. These limitations of conventional sonography have led to the use of other imaging modalities and invasive or costly procedures such as computed tomography (CT), magnetic resonance imaging (MRI) or biopsy. The availability of real-time contrast-enhanced ultrasound imaging (CEUS) has changed the strategy in the characterization of focal liver lesions, on healthy or cirrhotic liver in a neoplastic context or not, without inconvenience for the patient. The aim of the present study is to evaluate the place of CEUS in term of diagnostic relevance and catch of load cost, in the characterization of focal liver lesions detected but not characterized by CT or conventional sonography.