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).
Medical imaging plays a key role in the diagnostic and therapeutic management of renal cell carcinomas. It can be used to confirm the presence of a tumor, localize it, suggest malignancy or even histological subtype, guide sampling, perform TNM staging, assist in surgical scheduling, monitor therapeutic efficacy in the event of systemic treatment, guide ablathermy procedures and look for relapses after curative treatments have ended. Imaging of kidney tumors relies on three complementary imaging modalities: ultrasound (US), Computed Tomography based on X-ray absorption (CT-scan) and magnetic resonance imaging (MRI). The most commonly performed examination remains the CT scan, which is used to print 3D models. However, the correlation between renal parenchyma densities and renal tumors (before and during the scan acquisition times after injection) and elasticity parameters measured by US and MRI has never been explored. This trial aim to evaluate the correspondence between several imaging modalities for characterizing the elasticity of healthy and pathological kidney tissue. It will also improve the realism of 3D models used by surgeons, and identify new complementary imaging biomarkers. To achieve this aim, 50 patients will undergo DWI -elastography (Diffusion Weigthed Imaging-elastography) and MR-elastography sequences, as well as an US before the surgery. After surgery, a fragment of the resected specimen will be used to perform mechanical tests to determine the real hardness of the tissue.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
50
Imaging acquisition
Imaging acquisition
CHU de Bordeaux
Bordeaux, France
RECRUITINGMeasurement 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.
Time frame: Between Day 0 and Month 4
Measure of Spearman's rho
Researh of the highest possible value (maximum = 1).
Time frame: Between Day 0 and Month 4
Measure of the qualitative assessment
Measure based on a 5-point ordinal qualitative scale
Time frame: Between Day 0 and Month 4
Measure of the contrast
Calculation using the contrast-to-noise ratio
Time frame: Between Day 0 and Month 4
Measure of the noise
Calculation using the signal-to-noise ratio
Time frame: Between Day 0 and Month 4
Identification of imaging faisability limiting factors
Evaluation of the number of situations where the examination has no clinical diagnostic value and associations with potential limiting factors by using a composite criteria including : * body mass index * sarcopenia, * lesion location * lesion size * lesion architecture
Time frame: Between Day 0 and Month 4
Evaluation of imaging repetabiliy limiting factors
Measurement of the intra-class correlation coefficients and Bland-Altmann plot traces. The aim is for the intra-class score to be as high as possible (maximum = 1, ideally \>0.90).
Time frame: Between Day 0 and Month 4
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identification of decorrelation situations and potential bias between biomechanical properties obtained by hardness measurements in the different imaging modalities
Visual measurement for each patient and anatomical situation with possible matching, point clouds with in X the hardness obtained by one imaging modality and in Y the hardness obtained by another imaging modality. The descriptive characteristics of the points (voxel or patient segmentation) with decorrelation will then be analyzed.
Time frame: Between Day 0 and Month 4
Carry out associations between histological categorical variables and numerical elasticity variables
The measurement will be based on associations between categorical histological and numerical elasticity variables, with area under the ROC curve comparisons or cut-off identification where appropriate. Malignant/benign character (binary variable) and histological type (non- ordinal categorical variable) will be assessed as part of routine care by the pathologist.
Time frame: Between Day 0 and Month 4
Measure of the accuracy
Measure accordance between continuous scanner elasticity mapping (in kPa) and discrete elasticity mapping derived from density classes (necrosis, tissue, calcifications), assessed by Spearman's correlation coefficient and Bland-Altman analysis (bias and limits of agreement), with the objective being a high correlation and minimal bias.
Time frame: Between Day 0 and Month 4