AUTHOR=Wang Xiao-Jie , Qu Bai-Qiang , Zhou Jia-Ping , Zhou Qiao-Mei , Lu Yuan-Fei , Pan Yao , Xu Jian-Xia , Miu You-You , Wang Hong-Qing , Yu Ri-Sheng 

TITLE=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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.633034

DOI=10.3389/fonc.2021.633034

ISSN=2234-943X

ABSTRACT=Background: Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, posing a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf in computed tomography imaging.
Methods: A total of 202 patients (2011–2019) that were pathologically confirmed with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by randomly dividing them into a training cohort (n=142) and a validation cohort (n=60). A model was established by logistic regression, and it was weighted to be a scoring system. The ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three groups for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.
Results: Four computed tomography features were incorporated into the scoring system. 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). Patients with ccRCC among the three groups (≥0 to <2 points; ≥2 to ≤4 points; >4 to ≤11 points) significantly increased with increasing scores.
Conclusion: This scoring system is convenient for distinguishing between ccRCC and RAML-wvf by observing four critical computed tomography features.