Is fat quantification based on proton density fat fraction useful for differentiating renal tumor types?

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Renal tumors require accurate differentiation for appropriate treatment.
• Multiparametric kidney MRI offers advanced diagnostic capabilities.
• Fat quantification using proton density fat fraction (PDFF) is a promising technique.

Purpose of the Study:

• To assess the diagnostic accuracy of MRI-PDFF for differentiating various renal tumors.
• To evaluate the sensitivity and specificity of PDFF in distinguishing clear cell renal cell carcinomas (ccRCCs) from other renal masses.

Main Methods:

• Retrospective analysis of multiparametric kidney MRI data from 226 patients.
• Utilized mDIXON-Quant sequence for volumetric PDFF measurement in solid tumor components.
• Compared MRI-PDFF values with histopathological diagnoses, including ccRCCs, pRCCs, oncocytomas, chRCCs, lymphomas, uroepithelial tumors, and angiomyolipomas (AMLs).

Main Results:

• Clear cell RCCs exhibited a significantly higher mean MRI-PDFF (12.6%) compared to other renal cell carcinoma (RCC) types, uroepithelial tumors, lymphomas, and lipid-poor AMLs (p < 0.05).
• Lipid-rich AMLs showed a very high fat ratio.
• Measurements demonstrated good interobserver agreement.

Conclusions:

• MRI-PDFF is a valuable tool for the accurate differentiation of ccRCCs from other renal tumors.
• The technique offers high sensitivity and specificity for diagnosing ccRCCs.
• Fat quantification via PDFF enhances diagnostic precision in renal tumor characterization.