Radiomics

The EUS-AI-R study is an observational, single-center, two-phase (retrospective-prospective) study designed to develop and validate artificial intelligence (AI) models for predicting chemotherapy response and oncological outcomes in patients with pancreatic ductal adenocarcinoma (PDAC). Patients who underwent endoscopic ultrasound (EUS) with tissue acquisition (EUS-FNA/FNB) for suspected pancreatic lesions at IRCCS San Raffaele Hospital between January 1st, 2019 and January 2026 will be retrospectively included. All patients have a histologically confirmed diagnosis of PDAC and a minimum follow-up of six months. These data derive from an IRB-approved institutional study (BIOPANCREAS; NCT06552078). Retrospective multimodal data, including EUS imaging (B-mode, elastography, contrast-enhanced EUS), clinical and laboratory variables, CT/MRI imaging, digital pathology, and molecular data when available, will be used to develop and internally validate multiple AI models. In the prospective phase, the best-performing AI model will be applied to an independent cohort of patients undergoing EUS at the same institution to evaluate feasibility, calibration, and real-world performance. No additional procedures beyond standard clinical practice will be performed.

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

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.

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.

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.

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.

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.

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