A Rare Benign Tumor in a 14-Year-Old Girl

Abualjadayel, Meral; Safdar, Osama Y; Banjari, Maysaa A.; Desoky, Sherif El; Mokhtar, Ghadeer; Azhar, Raed A.

doi:10.1155/2018/1548283

Cited by 2 publications

(1 citation statement)

References 18 publications

(20 reference statements)

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“…Renal oncocytomas and angiomyolipomas are benign renal neoplasms that usually present as incidental findings on imaging [1]. Of all resected renal tumors, renal oncocytomas account for 3% to 7%, and angiomyolipomas constitute about 1% [2,3]. To the best of our knowledge, the co-occurrence of angiomyolipoma with oncocytoma is infrequent and has been reported in only 17 cases, with 3 cases in the context of tuberous sclerosis [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Rare Case of Synchronous Oncocytoma and Angiomyolipoma of the Kidney

2022

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Objective:Rare disease Background:The co-occurrence of renal oncocytoma and angiomyolipoma is exceedingly rare. To date, 17 such cases have been reported in the literature. This report describes a unique case of that association that presented as a single renal mass on imaging. Case Report:A 75-year-old woman presented with epigastric discomfort. A CT scan of the abdomen revealed a 6.6×5.7×4.7 cm enhancing right renal mass. Gross examination revealed a nodular, well-circumscribed, tan-brown mass located in the lower pole of the kidney that was abutting the renal capsule. Interestingly, superior to this mass, there was an adjacent, pale tan-white, firm, well-circumscribed nodule in the mid-pole, which was not detected on the CT scan and grossly extended to 1.1 cm of the overlying renal capsule. Histologically, the larger tumor showed characteristic features of oncocytoma. The smaller tumor had an admixture of mature adipose tissue, smooth muscle, and vessels, consistent with a renal angiomyolipoma. Conclusions:We present a new case of synchronous renal angiomyolipoma and oncocytoma, which were uniquely adjacent and coexisted with minimal intermingling renal parenchyma. Other "eosinophilic renal tumors" are significant differential diagnosis considerations. Due to the close proximity of these lesions, this association can present clinically and radiologically as a single renal mass. Careful examination of the nephrectomy specimen is essential for the proper detection of small-sized tumors.

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Section: Introductionmentioning

confidence: 99%

A Rare Case of Synchronous Oncocytoma and Angiomyolipoma of the Kidney

2022

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Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset

Klontzas,

Kalarakis,

Koltsakis

et al. 2024

Insights Imaging

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Objectives To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset. Methods A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN. Results Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873–0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752–0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844–0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma. Conclusion Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors. Critical relevance statement Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors. Key points • Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors. Graphical Abstract

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A Rare Benign Tumor in a 14-Year-Old Girl

Cited by 2 publications

References 18 publications

A Rare Case of Synchronous Oncocytoma and Angiomyolipoma of the Kidney

A Rare Case of Synchronous Oncocytoma and Angiomyolipoma of the Kidney

Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset

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