View clinical trials related to Radiomics.
Filter by:Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. In this study, a deep learning algorithm based on CT imaging and nomogram was proposed to identify and predict the presence of adherent perirenal fat. This study includes the construction of a prediction model based on CT imaging and the verification of the prediction model.
The goal of this observational study is to explore the role of prediction of microvascular invasion by radiomics based on pre-treatment magnetic resonance imaging for guiding treatment of Barcelona Clinic Liver Cancer stage B hepatocellular carcinoma.
The purpose of this study is to compare AI performance to doctor's performance in the evaluation of IPF/UIP and ILDs without UIP(proven by biopsy).
To predict prostate cancer and its prognosis by ultrasound radiomics in ultrasound fusion prostate targeted biopsy.
The purpose of this study is to evaluate the performance of a PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma based on a multicenter prospective cohort.
A test-retest study on the stability and repeatability of healthy skin features on OCT
This study aims to assess multimodal Radiomics-based prediction model for prognostic prediction in spinal tumors.
Crohn's disease (CD), a type of inflammatory bowel disease (IBD), is a chronic intestinal recurrent inflammatory disease involving the entire digestive tract. Most CD patients require surgery for complications, including stenosis, perforation, and severe intestinal bleeding. Predicting early-onset surgery risk is of great importance to assist launching of therapeutic strategies. We aim to establish a digital prognostic model and nomogram using radiomics, which will help clinical practice.
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 :