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Updated: Apr 13, 2026
In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
Is fat quantification based on proton density fat fraction useful for differentiating renal tumor types?
Canan Altay1, Işıl Başara Akın2, Hakan Abdullah Özgül3
1Dokuz Eylül University, Izmir, Turkey. drcananaltay@gmail.com.
Purpose:
This study retrospectively assessed the diagnostic accuracy of fat quantification based on proton density fat fraction (PDFF) for differentiating renal tumors.
Methods:
In this retrospective study, 98 histologically confirmed clear cell renal cell carcinomas (ccRCCs), 35 papillary renal cell carcinomas (pRCCs), 14 renal oncocytomas, 16 chromophobe renal cell carcinomas (chRCCs), 10 lymphomas, 19 uroepithelial tumors, 10 lipid-poor angiomyolipomas (AMLs), and 25 lipid-rich AMLs were identified in 226 patients (127 males and 99 females) over 5 years. All patients underwent multiparametric kidney MRI. The MRI protocol included an axial plane and a volumetric 3D fat fraction sequence known as mDIXON-Quant for PDFF measurement. Demographic data were recorded, and PDFF values were independently reviewed by two radiologists blinded to pathologic results. MRI examinations were performed using a 1.5 T system. MRI-PDFF measurements were obtained from the solid parts of all renal tumors. Fat quantification was performed using a standard region of interest for each tumor, compared to histopathological diagnoses. Sensitivity and specificity analyses were performed to calculate the diagnostic accuracy for each histopathological tumor type. Nonparametric variables were compared among the subgroups using the Kruskal-Wallis H test and Mann Whitney U test. P-values < 0.05 were considered statistically significant.
Results:
In all, 102 patients underwent partial nephrectomy, 70 patients underwent radical nephrectomy, and the remaining 54 had biopsies. Patient age (mean: 58.11 years; range: 18-87 years) and tumor size (mean: 29.5 mm; range: 14-147 mm) did not significantly differ across groups. All measurements exhibited good interobserver agreement. The mean ccRCC MRI-PDFF was 12.6 ± 5.06% (range: 11.58-13.61%), the mean pRCC MRI-PDFF was 2.72 ± 2.42% (range: 2.12-3.32%), and the mean chRCC MRI-PDFF was 1.8 ± 1.4% (range: 1.09-2.5%). Clear cell RCCs presented a significantly higher fat ratio than other RCC types, uroepithelial tumors, lymphomas, and lipid-poor AMLs (p < 0.05). Lipid-rich AMLs demonstrated a very high fat ratio.
Conclusion:
MRI-PDFF facilitated accurate differentiation of ccRCCs from other renal tumors with high sensitivity and specificity.
