Renal Cell Carcinoma: Accuracy of Multidetector Computed Tomography in the Assessment of Renal Sinus Fat Invasion.

Abstract

PURPOSE The purpose of this study was to evaluate the accuracy of multidetector computed tomography (MDCT) in the preoperative assessment of renal sinus fat invasion (RSFI) in patients with renal cell carcinoma (RCC) and to assess imaging features that improve detection of RSFI on CT. METHODS This is a single-institution retrospective review of 53 consecutive patients with histologically proven RCC who underwent triple-phase preoperative contrast MDCT prior to partial or radical nephrectomy. Two experienced radiologists (R1 and R2), blinded to the final histology result, independently reviewed the preoperative MDCT studies to assess for RSFI. Histopathology was used as the gold standard for the presence of RSFI. RESULTS Of 55 surgically resected RCCs that were evaluated with contrast-enhanced MDCT, 34.5% (19/55) of RCCs had RSFI on final histopathology. Multidetector CT demonstrated high sensitivity (R1, 100%; R2, 93.7%) for the detection of RSFI, but a low positive predictive value (R1, 40%; R2, 53%) and specificity (R1, 38.4%; R2, 66.6%). Interreader agreement for RSFI was moderate (κ = 0.56). Renal tumors were significantly larger in cases with RSFI (6.3 ± 3.219 cm) than tumors without RSFI (4.1 ± 2.9 cm) (P = 0.0275). Renal sinus fat invasion was more commonly associated to an irregular tumor margin at the tumor renal sinus fat interface (P < 0.001). CONCLUSIONS Multidetector computed tomography demonstrates a high sensitivity but low positive predictive value in diagnosing RSFI with implications for prognosis and treatment planning. Tumor size, location, irregular tumor margin at the tumor/renal sinus interface, and invasion into pelvicaliceal structures can aid in the diagnosis of RSFI on preoperative MDCT.

Cite this paper

@article{Bolster2016RenalCC, title={Renal Cell Carcinoma: Accuracy of Multidetector Computed Tomography in the Assessment of Renal Sinus Fat Invasion.}, author={Ferdia Bolster and Laura Durcan and Ciara Barrett and Leo P. Lawler and Carmel Geraldine Cronin}, journal={Journal of computer assisted tomography}, year={2016}, volume={40 6}, pages={851-855} }