Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques
Motivation: Accurate preoperative grading of clear cell renal cell carcinoma (ccRCC) is essential for treatment decisions, particularly in patients with comorbidities.
Goal(s): This study aims to investigate the utility of various diffusion-weighted imaging (DWI) models, including mono-exponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and continuous time random walk (CTRW), in preoperative ccRCC grading to facilitate personalized treatment strategies.
Approach: We conducted MRI scans on 105 ccRCC patients with multi-b value DWI and employed machine learning for model construction for ccRCC grading.
Results: This study demonstrates the protential of multi-parametric DWI models for accurate ccRCC grading, yield an AUC of 0.861.
Impact: This study has the potential to reshape the landscape of preoperative ccRCC grading, promote objectivity in treatment planning.
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