View clinical trials related to Radiomics.
Filter by:This project intends to use multiple types of biological samples from glioma patients and mouse intracranial tumor models as research objects, and comprehensively apply a series of omics sequencing technologies and molecular biology technologies to jointly define the following research objectives :
To assess the potential usefulness of radiogenomics for tumor driving genes heterogeneity in non-small cell lung cancer.
Little is known about the correlation between ultrasound characteristics (conventional, elastography and contrast enhanced ultrasound (CEUS) )and pathological prognostic factors in breast cancer. The aim of this study was to explore the correlation between ultrasound characteristics and pathological prognostic factors using radiomics.
This study is aimed to illustrate whether Radiomics combining multiparametric MRI before and after neoadjuvant chemotherapy (NACT) with clinical data is a good way to predict axillary lymph node metastasis and prognosis in invasive-breast-cancer.
This bi-directional, multicentre study aims to assess multiparametric MRI Radiomics-based prediction model for identifying metastasis lymph nodes and prognostic prediction in breast cancer.
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.
This is a single-arm, multicentre study that aims to assess whether Radiomics combining multiparametric MRI and clinical data could be a good predictor of the responses to neoadjuvant chemotherapy in Breast Cancer.