Clinical Research Directory

Application of Ultrasound Radiomics in Ultrasound Fusion Targeted Prostate Biopsy

To predict prostate cancer by ultrasound radiomics in ultrasound fusion prostate targeted biopsy.

Multimodal Exercise Therapy for Non-Surgical Intervention of Nonspecific Low Back Pain.

This multicenter, assessor-blinded, two-arm parallel randomized controlled trial (N = 314) will compare the efficacy and safety of a 6-week multidimensional exercise program plus usual pharmacological care (experimental arm) versus usual pharmacological care alone (control arm) in adults ≥ 60 years with chronic non-specific low-back pain (LBP) and imaging evidence of paraspinal muscle degeneration. The primary endpoint is change in Oswestry Disability Index (ODI) at 12 months. Secondary endpoints include pain VAS, JOA score, recurrence rate, and patient satisfaction measured repeatedly to 12 months. Advanced MRI radiomics and machine-learning algorithms will be used to build a "paraspinal muscle imaging-function-prognosis" prediction model and an open-access web tool for risk stratification. The study will generate a standardized, evidence-based non-operative care pathway for chronic LBP driven by paraspinal muscle degeneration

Predicting HIF-2α Levels in Clear Cell Kidney Cancer Using Machine Learning

This project aims to conduct a multicenter retrospective study to collect clinical, CT imaging, and pathological data from patients. A comprehensive data management system will be established, and radiomic features will be extracted to integrate and analyze multicenter data. We will develop a predictive model based on CT radiomic features and perform both internal and external cohort validation. The model will predict HIF-2α expression levels and clinically relevant prognostic factors in ccRCC, enabling precise identification of patient populations responsive to the HIF-2α antagonist Belzutifan, thereby facilitating personalized treatment decisions, minimizing unnecessary therapeutic risks, and ultimately improving patient quality of life and clinical outcomes.

Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma

This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.

RADIomics to Predict HER2 Status And T-DXd Efficacy in Metastatic Breast Cancer: the RADIOSPHER2 Study

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.

Radiomics-Based AI Model for Predicting Para-Aortic Lymph Node Metastasis in Gastric Cancer Patients

This study aims to develop and validate an artificial intelligence (AI) model based on radiomics features extracted from preoperative CT images to predict para-aortic lymph node (PALN) metastasis in patients with gastric cancer. Accurately identifying PALN metastasis before surgery can help doctors make better treatment decisions, such as whether to proceed with surgery, consider chemotherapy, or use other treatment strategies. The study will prospectively enroll patients who are diagnosed with gastric cancer and scheduled for surgery. All participants will undergo routine imaging tests, and their data will be analyzed using advanced AI techniques. The results of this study may improve the precision of preoperative staging and support personalized treatment planning for gastric cancer patients.

Dixon MRI Imaging Histology for Predicting Postoperative Infection in Posterior Lumbar Fusion for Myasthenia Gravis

Sarcopenia is an age-related condition that manifests itself as a persistent decrease in muscle mass and function, which may lead to decreased physical function, increased risk of disease, and reduced quality of life. In surgical patients, sarcopenia has been shown to be associated with increased postoperative complications, prolonged hospitalization, and decreased survival. In patients undergoing lumbar fusion, the presence of sarcopenia may increase the risk of postoperative infection.Lumbar fusion is a common procedure to treat lumbar spine disorders such as lumbar disc herniation, lumbar spondylolisthesis, or lumbar spinal stenosis. However, this procedure is associated with a high rate of complications, especially postoperative infections, which can lead to reoperation, prolonged hospitalization, and even affect patient survival. In recent years, more and more studies have found a significant association between sarcopenia and postoperative infections after lumbar fusion surgery.Imagingomics belongs to a branch of machine learning, which is the process of acquiring images from various imaging devices such as CT, MRI and ultrasound, outlining the region of interest through image segmentation, extracting the features of the image within the region of interest, downscaling the features, and finally building an imagingomics model. MRI Imagingomics, utilizing its rich data information, has shown great potential for application in several medical fields. Among them, the accuracy of Dixon MRI imaging histology in muscle mass and texture assessment makes it particularly important in predicting postoperative infections.Based on this, it is reasonable to believe that Dixon MRI imaging histology can be a powerful tool to help us predict a patient's risk of postoperative infection.

Neurophenotype Predicts CD Disease Progression

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.

Application of Radiomics in Breast 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.

Stereotactic Radiosurgery Prognosis Assessment for Spinal Tumors Based on Radiomics

This study aims to assess multimodal Radiomics-based prediction model for prognostic prediction in spinal tumors.