Introduction: Assessment of renal tissue and renal tumor stiffness may provide complementary information for tissue characterization; however, conventional imaging modalities such as multiphasic computed tomography (CT) do not directly quantify biomechanical properties. Elastography techniques, including magnetic resonance elastography (MRE) and ultrasound elastography (US-E), allow noninvasive measurement of tissue stiffness but are not routinely available in standard clinical practice. This study protocol aims to develop a CT-based stiffness mapping of renal parenchyma and renal tumors by investigating the relationship between CT attenuation values and elastography-derived stiffness measurements, using MRE and US-E as reference modalities.
Methods and analysis: This monocentric, prospective, exploratory, non-randomized, and non-blinded diagnostic study will enroll 50 adults undergoing partial or radical nephrectomy for renal tumors at the University Hospital of Bordeaux. All participants will undergo a predefined multimodal imaging protocol-including contrast-enhanced CT, multiparametric magnetic resonance imaging (MRI) with -MRE and US-E-conducted between inclusion and the day before surgery. The primary objective is to construct a regression model predicting MRE-derived elasticity (μMRE) from CT density values using multiple machine-learning algorithms evaluated through repeated nested cross-validation. Secondary analyses will include voxel-level and region-of-interest correlations across modalities, feasibility and image-quality assessment of DWI-vMRE, repeatability of elastography measurements, identification of limiting factors such as BMI, sarcopenia, lesion location and architecture, evaluation of inter-modality de-correlation and associations with final histopathology (including subtype and grade).
Trial registration: ClinicalTrials.gov identifier: NCT06525831. Protocol ID-RCB: 2024-A00959-38. Recruitment began on 7 March 2025.
Copyright: © 2026 Ronca et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.