Munich, Germany (UroToday.com) Incidence and prevalence of kidney cancer worldwide remain concerning. Due to the increased use of cross-sectional imaging technology, the number of incidentally diagnosed small renal masses has significantly increased. Literature has demonstrated that approximately 20% of these masses are benign and these include oncocytomas and angimyolipomas (AML).
The role of imaging in diagnosing and differentiating renal mass histopathology is important. Tanaka and colleagues proposed a practical diagnostic algorithm for the diagnosis of AML and other types of renal cancer using computer tomography (CT) and magnetic resonance imaging (MRI).
The study was performed in a prospective fashion at 2 academic institutions in Japan. A total of 153 solid renal masses less than 4 cm were included in the study. All patients were imaged using CT or MRI prior surgical removal of the tumor. First, based on the CT and clinical findings, a prediction model for AML was developed using multivariate analysis (CT model). With additional MRI findings, another prediction model for AML was developed (CT+MRI model). The diagnostic algorithm for AML with sequential use of the two models was constructed, and was externally validated using another cohort of 66 SRMs, which included 7 AMLs and 5 oncocytomas.
Authors found several interesting factors to be associated with primary outcome. They reported that in the CT model, independent predictors of AML were age female <50 years old, degree of attenuation on unenhanced CT imaging, less enhancement than normal renal cortex on corticomedullary phase (CMP) of CT and CT homogeneity on CMP.
Overall, the CT model was able to differentiate 102 tumors, which met none of the factors, as a low AML-probability group, while the other 51 tumors were candidates for the CT+MRI model. In the CT+MRI model, the first 3 factors of the CT model, low signal intensity on T2W-MRI and absence of pseudocapsule on T2W-MRI were independent predictors. “Female <50 years old” was scored as 2 points and the other 4 factors were scored as 1 point each. The CT+MRI model then stratified the 51 tumors into low (total score 0–1), intermediate (2–3) and high (4–6) AML-probability groups. After the two-step stratification, the probabilities of AML were 0%, 28%, and 93% in the low, intermediate and high AML-probability groups, respectively. In the validation cohort, the probabilities were 0%, 21% and 75%, respectively.
In conclusion, this novel stepwise algorithm was able to differentiate AML from RCC with high accuracy. This may be cost-effective and provide an accurate preoperative. External validation studies are necessary to elucidate the role of this algorithm in the diagnosis and management of small renal masses.
Authors: Tanaka H et al.
1. Tokyo Medical and Dental University, Dept. of Urology, Tokyo, Japan,
2. Cancer Institute Hospital, Japanese Foundation For Cancer Research, Dept. of Urology, Tokyo, Japan
