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

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.

Metastasis and Recurrence Retroperitoneal Angiomyolipoma: a Case Report

“…Diagnosing ERAML is a challenge due to its similarity to benign and malignant tumors in this area, such as lipoma, leiomyoma, adrenal adenocarcinoma, renal cell carcinoma, and retroperitoneal liposarcoma [5]. The best imaging for diagnosing renal angiomyolipoma is CT and CTA [3.6] In Wang et al's study, CT features for differentiating RAML from liposarcoma, such as intratumoral linear vessels, aneurysmal dilation of intramural vessels, beak signs, hematomas, and discrete internal and intrarenal fatty tumors, are mentioned [7]. An MRI with CT can also help in diagnosing the connection of renal vessels with ERAML [8].…”

High peripheral neutrophil and monocyte count distinguishes renal cell carcinoma from renal angiomyolipoma and predicts poor prognosis of renal cell carcinoma

“…In terms of differentiating between RCC and RAML, Computed tomography(CT) is generally effective due to the presence of significant adipose tissue in most renal angiomyolipomas, resulting in low signal on CT images. Nevertheless, when distinguishing RCC from RAML without adipose tissue, there is a substantial decrease in imaging efficacy [ 29 ]. For example, in the majority of cases of epithelioid RAML, there is a paucity or absence of fat content, leading to potential confusion with classical renal cell carcinoma.…”

A diagnostic scoring model of ENKTCL in the nose-Waldeyer’s ring based on logistic regression: Differential diagnosis from DLBCL

“…According to the optimal value of AUC, when the value was assigned to 10, the diagnostic accuracy reached 91.7%, which greatly improved the clinically diagnostic accuracy of ENKTCL and the effect of its differentiation with DLBCL. The score model is a new points-scoring system for disease diagnosis pioneered by our research team, and it has been reported in many of our SCI papers and recognized internationally ( 16 – 18 ).…”