ArticleFrontiers in oncology2021
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.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Differentiation of Fat-Poor Renal Angiomyolipoma From Clear Cell Renal Cell Carcinoma: Diagnostic Performance of a Novel Type of Color Contrast Enhanced Ultrasound.Cancer medicine · 2026Article
- Comparison of perioperative efficacy and indications between laparoscopic capsule-preserving resection and partial nephrectomy for renal angiomyolipoma: a decade-long retrospective study.World journal of surgical oncology · 2025Article
- CT-based scoring system for diagnosing eosinophilic solid and cystic renal cell carcinoma versus clear cell renal cell carcinoma.Scientific reports · 2025Article
- Differential diagnosis of clear cell renal cell carcinoma with low signal intensity on T2WI from angiomyolipoma without visible fat on MR imaging.Frontiers in oncology · 2025Article
- Article
- A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL.Frontiers in oncology · 2023Article
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Abstract
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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