View clinical trials related to Radiomics.
Filter by:The goal of this observational study is aimed to develop a novel multimodal neuroimaging-based model to characterize the neurophenotype of Crohn's Disease patients and assess its ability for predicting disease progression, using multiomics data to interpret the model. Participants will be followed-up of at least six months for patients without disease progression to assess the relationship between neurophenotype and intestinal outcomes.
The aim of this study was to develop an radiomic model based on CT images to evaluate markers of the bladder cancer microenvironment, such as TSR,TIL, and IP. Secondly, the association of the radiomic model with clinical outcomes and immunotherapy response was investigated.
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.