Abstract
Around 10% of renal cell carcinomas (RCC) are cystic, while some benign cysts have complex appearance in conventional diagnostic tests such as computed tomography (CT) or magnetic resonance imaging (MRI). These renal complex cystic masses (RCCMs) are a challenging entity in urological practice and sometimes have a difficult management, requiring surgical removal. Contrast-enhanced ultrasound (CEUS) is a very sensitive test detecting microvascularization in real time, and it has been used in the diagnostic workup of these kinds of lesions. The aim of our study was to assess the diagnostic power of CEUS in the evaluation of RCCM. This is a prospective observational study between April 2011 and July 2014. A total of 66 patients with 67 RCCMs were enrolled (Bosniak 2-4). Twenty-four patients underwent surgical removal of the RCCM. All participants underwent CEUS (experimental) and CT (control). All CEUS procedures were performed by a single high-experienced observer (urologist). Benign lesions were defined as those Bosniak 2-2F, and malignant were Bosniak 3-4. Statistical analysis was made measuring consistency (kappa index and Landis-Koch scale) and validity (sensitivity, specificity, positive and negative predictive values) of the study. Median size of RCCM measured by CEUS and CT was 3.8cm (interquartile range (AIQ) 3.2-4.6) and 3.9cm (AIQ 3.2-4.5), respectively. Kappa index shows good agreement between both tests (0.71; 95% CI 0.57-0.85), both overall and stratified by categories according to Bosniak classification. CEUS has a sensitivity 100%, specificity 81.4%, positive predictive value 70.4%, and negative predictive value 100%. A total of eight RCCMs were discordant, and seven of eight classified as malignant by CEUS and not by CT. Of those seven lesions classified as malignant by CEUS, six (six of seven, 85.7%) were malignant in the pathological exam. CEUS is avery useful toolfor assessing RCCM, with good results in terms of consistency and validity. It has a good diagnostic power, with a sensitivity of 100% and a negative predictive value of 100%. Its main limitations are the experience required, a special software, and being observer-dependent.
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