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NCT07655648
This single-center retrospective study aims to develop an interpretable radiomics model based on dual-tracer PET/CT to preoperatively predict the postoperative pathological Gleason grade group in treatment-naïve prostate cancer patients. A machine learning-based three-class prediction model will be constructed and interpreted using SHAP. Its performance will be compared with systematic biopsy results, assessing grading accuracy and prognostic value for biochemical recurrence-free survival.
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.
NCT07269535
In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids. However, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.
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.
NCT07030569
RADIOSPHER2 study is a monocentric, retrospective, observational study aiming at identifying a radiomics signature able to predict HER2 expression (0 vs low vs overexpression) and trastuzumab deruxtecan efficacy in metastatic breast cancer patients. The study also encompasses translational analyses and inter-modal correlations in order to provide novel insights about HER2 spatial and temporal heterogeneity, at the macroscopic and microscopic levels.
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.