Feasibility study on the value of PET/MRI compared to PET/CT in oncological patients with a molecularly oriented individualized therapy concept
Not Applicable
- Conditions
- C00-C97Malignant neoplasms
- Registration Number
- DRKS00024032
- Lead Sponsor
- niversitätsklinikum Tübingen
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting withdrawn before recruiting started
- Sex
- All
- Target Recruitment
- 50
Inclusion Criteria
• Presence of a tumor disease and presentation of the patient in the Molecular Tumor Board (MTB) of the University Hospital Tübingen
• Performing a PET/CT based on a clinical indication (MTB)
• The patient tolerates about 60 minutes of lying in the tomograph
• Age =18 years
• Written consent from the patient
Exclusion Criteria
• Pregnant / breastfeeding women
• Presence of non-MR compatible implants or any kind of metal in and on the body
• Patients who report a hearing impairment or an increased sensitivity to loud noises
• Patients with limited temperature sensation
• Claustrophobia
• Limited ability to give consent
• Obesity with a body weight of> 150 kg
• No consent to the communication of incidental findings
Study & Design
- Study Type
- interventional
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Collection of 50 tumor patient-related image data sets, consisting of morphological and functional PET/MRI image data of the primary tumor. The image data are correlated with existing corresponding histopathological and molecular biological information with the aim of developing image-based marker sets for molecular tumor characterization.
- Secondary Outcome Measures
Name Time Method • Tumor subtyping based on multiparametric image data with regard to prognosis and prediction factors such as receptor status, TNM grading<br>• Correlation and voxel-by-voxel analysis of the PET/MRI data sets with corresponding molecular (bio) markers with the aim of mapping three-dimensional maps of various functional and morphogenetic parameters<br>• Development and clinical application of machine learning approaches, e.g. for texture analysis