About this trial
The goal of this innovative project is to evaluate the correspondence between several imaging modalities for characterizing the elasticity of healthy and pathological renal tissue which could help improve the realism of 3D prints used by urological surgeons and allow the identification of new, complementary imaging biomarkers. The main objective is to develop a predictive model of the biomechanical properties of normal and pathological kidney tissue, as assessed by the reference method (Magnetic Resonance (MR)-elastography).
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Adult patients (≥ 18 years of age)
Scheduled for surgical management with nephrectomy for kidney tumor in the urology department of Bordeaux University Hospital
CT scan available or scheduled for surgery
Consent expressed for integration of the UroCCR database
Disqualifiers
- Pregnant or breast-feeding women
Contraindication to MRI (Magnetic Resonance Imaging)
Contraindication to injection of gadoline contrast agents
Biopsy performed within 15 days prior to MRI, CT and ultrasound scans (risk of artifactual alteration, via iatrogenic hemorrhagic changes, of the biomechanical properties of the renal tumor and parenchyma).
Trial design
Single group
Treatments tested in this trial
MR-elastography exam
Diagnostic testImaging acquisition
Ultrasound exam
Diagnostic testImaging acquisition
Treatment groups
Trial outcomes
Primary outcomes
Measurement of the root mean square error (RMSE)
The aim is to successfully predict μMRE from dCT-, dCT40s, dCT90s, dCT10min (naming μCT the scan model prediction) with the lowest possible error.
Secondary outcomes
Measure of Spearman's rho
Researh of the highest possible value (maximum = 1).
Measure of the qualitative assessment
Measure based on a 5-point ordinal qualitative scale
Measure of the contrast
Calculation using the contrast-to-noise ratio
Measure of the noise
Calculation using the signal-to-noise ratio
Sponsors and contacts
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University Hospital, Bordeaux
Lead sponsor
Sophia Genetics SAS
Collaborator