Loading clinical trials...
Loading clinical trials...
Showing 1-7 of 7 trials
NCT07531446
The investigators investigated the associations between the imaging parameters of ⁶⁸Ga-FAPI and ¹⁸F-FDG dual-tracer PET/CT and concomitant interstitial lung disease (ILD) in patients with dermatomyositis (DM), developed a novel diagnostic model to predict DM complicated with ILD, and conducted external validation of this model. Meanwhile, the investigators compared the predictive performance of the imaging-only model with that of the classic clinical model and the clinical-radiological collaborative model.
NCT07263373
Bronchiectasis is a heterogeneous condition with diverse etiologies and clinical manifestations. Its progression involves a vicious cycle of airway inflammation, recurrent infection, and structural damage, leading to persistent symptoms and declining lung function. Current management focuses on airway clearance and antibiotics, with no disease-modifying therapies available. Recognizing this heterogeneity is crucial for advancing targeted treatments and precision medicine. Radiomics converts medical images into mineable data to reveal underlying pathophysiology. While applied in other respiratory diseases, its potential in bronchiectasis remains underexplored. Both radiomics and the lung microbiome are independently linked to disease severity in conditions like COPD, but their interplay is unclear. Integrating these modalities with clinical data could unlock novel insights, identify new therapeutic targets, and improve diagnostic and prognostic models. However, few studies have investigated multimodal models combining radiomics, microbiome, and clinical features to predict outcomes in bronchiectasis. To address this gap, we designed a multicenter, retrospective study. It will analyze data from patients diagnosed between January 2020 and July 2025 to evaluate the combined value of radiomics, microbial features, and clinical parameters in diagnosing and predicting the progression of bronchiectasis.
NCT06452550
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.
NCT06062173
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.
NCT05889949
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.
NCT04792437
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 :
NCT03592004
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.