scholarly journals Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography

2016 ◽  
Vol 11 (3) ◽  
pp. 2327-2331 ◽  
Author(s):  
PINGKUN XIE ◽  
ZHIHUI YANG ◽  
ZHENG YUAN
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Renal Angiomyolipoma ◽  
Cell Renal Cell Carcinoma ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

scholarly journals Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography

SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Kousei Ishigami ◽  
Leandro V. Leite ◽  
Marius G. Pakalniskis ◽  
Daniel K. Lee ◽  
Danniele G. Holanda ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Tumor Grade ◽  
Cell Renal Cell Carcinoma ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

scholarly journals A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Jie Wang ◽  
Bai-Qiang Qu ◽  
Jia-Ping Zhou ◽  
Qiao-Mei Zhou ◽  
Yuan-Fei Lu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Scoring System ◽  
Clear Cell ◽  
Renal Angiomyolipoma ◽  
Cell Renal Cell Carcinoma ◽  
Number Of Patients ◽  
Primary Model

BackgroundRenal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.MethodsA total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.ResultsFour independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores.ConclusionThis scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

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