Symptomatic giant renal oncocytoma with an incidental papillary adenoma

Abstract Background Primary Seminal Vesicle (SV) tumours are a rare entity, with most SV masses representing invasion of the SV by malignancy originating in an adjacent organ, most often the prostate. Previously reported primary SV epithelial tumours have included adenocarcinoma and cystadenoma, with limited prior reports of inracystic papillary structures. Case presentation A 35-year-old male presented with azoospermia, intermittent macroscopic haematuria, and mild right iliac fossa and groin pain. A papillary appearing seminal vesicle mass was found on imaging and seminal vesicoscopy. The mass was robotically excised with diagnosis of benign cystic papillary adenoma made. Conclusion In this manuscript we describe a rare case of a benign cystic papillary adenoma of the seminal vesicle, a unique histological entity differentiated from cystadenoma of the Seminal Vesicle by its papillary component.

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Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Abdominal Radiology ◽

10.1007/s00261-019-02269-9 ◽

2019 ◽

Vol 45 (10) ◽

pp. 3193-3201 ◽

Cited By ~ 3

Author(s):

Yajuan Li ◽

Xialing Huang ◽

Yuwei Xia ◽

Liling Long

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Area Under The Curve ◽

Image Features ◽

Renal Tumors ◽

Support Vector ◽

Svm Classifier ◽

Renal Oncocytoma ◽

Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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Magnetic resonance diffusion kurtosis imaging in differential diagnosis of benign and malignant renal tumors

Cancer Imaging ◽

10.1186/s40644-020-00369-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jianxiong Fu ◽

Jing Ye ◽

Wenrong Zhu ◽

Jingtao Wu ◽

Wenxin Chen ◽

...

Keyword(s):

Differential Diagnosis ◽

Mean Diffusivity ◽

Threshold Value ◽

Renal Tumors ◽

Diffusion Kurtosis Imaging ◽

Renal Oncocytoma ◽

Renal Angiomyolipoma ◽

Planar Imaging ◽

Cell Renal Cell Carcinoma ◽

Diffusion Kurtosis

Abstract Background Benign and malignant renal tumors share similar some imaging findings. Methods Sixty-six patients with clear cell renal cell carcinoma (CCRCC), 13 patients with renal angiomyolipoma with minimal fat (RAMF) and 7 patients with renal oncocytoma (RO) were examined. For diffusion kurtosis imaging (DKI), respiratory triggered echo-planar imaging sequences were acquired in axial plane (3 b-values: 0, 500, 1000s/mm2). Mean Diffusivity (MD), fractional Anisotropy (FA), mean kurtosis (MK), kurtosis anisotropy (KA) and radial kurtosis (RK) were performed. Results For MD, a significant higher value was shown in CCRCC (3.08 ± 0.23) than the rest renal tumors (2.93 ± 0.30 for RO, 1.52 ± 0.24 for AML, P < 0.05). The MD values were higher for RO than for AML (2.93 ± 0.30 vs.1.52 ± 0.24, P < 0.05), while comparable MD values were found between CCRCC and RO (3.08 ± 0.23 vs. 2.93 ± 0.30, P > 0.05). For MK, KA and RK, a significant higher value was shown in AML (1.32 ± 0.16, 1.42 ± 0.23, 1.41 ± 0.29) than CCRCC (0.43 ± 0.08, 0.57 ± 0.16, 0.37 ± 0.11) and RO (0.81 ± 0.08, 0.86 ± 0.16, 0.69 ± 0.08) (P < 0.05). The MK, KA and RK values were higher for RO than for CCRCC (0.81 ± 0.08 vs. 0.43 ± 0.08, 0.86 ± 0.16 vs. 0.57 ± 0.16, 0.69 ± 0.08 vs. 0.37 ± 0.11, P < 0.05). Using MD values of 2.86 as the threshold value for differentiating CCRCC from RO and AML, the best result obtained had a sensitivity of 76.1%, specificity of 72.6%. Using MK, KA and RK values of 1.19,1.13 and 1.11 as the threshold value for differentiating AML from CCRCC and RO, the best result obtained had a sensitivity of 91.2, 86.7, 82.1%, and specificity of 86.7, 83.2, 72.8%. Conclusion DKI can be used as another noninvasive biomarker for benign and malignant renal tumors’ differential diagnosis.

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Clinical Implications of (Pro)renin Receptor (PRR) Expression in Renal Tumours

Diagnostics ◽

10.3390/diagnostics11020272 ◽

2021 ◽

Vol 11 (2) ◽

pp. 272

Author(s):

Jon Danel Solano-Iturri ◽

Enrique Echevarría ◽

Miguel Unda ◽

Ana Loizaga-Iriarte ◽

Amparo Pérez-Fernández ◽

...

Keyword(s):

Renal Cell ◽

Tumour Progression ◽

Renin Angiotensin System ◽

Staging System ◽

Staining Intensity ◽

Renal Oncocytoma ◽

Angiotensin System ◽

Promising Therapeutic Target ◽

Renal Tumours ◽

Grade Tumour

(1) Background: Renal cancer is one of the most frequent malignancies in Western countries, with an unpredictable clinical outcome, partly due to its high heterogeneity and the scarcity of reliable biomarkers of tumour progression. (Pro)renin receptor (PRR) is a novel receptor of the renin–angiotensin system (RAS) that has been associated with the development and progression of some solid tumours by RAS-dependent and -independent mechanisms. (2) Methods: In this study, we analysed the immunohistochemical expression of PRR at the centre and border in a series of 83 clear-cell renal cell (CCRCCs), 19 papillary (PRCC) and 7 chromophobe (ChRCC) renal cell carcinomas, and the benign tumour renal oncocytoma (RO, n = 11). (3) Results: PRR is expressed in all the tumour subtypes, with higher mean staining intensity in ChRCCs and ROs. A high expression of PRR at the tumour centre and at the infiltrative front of CCRCC tissues is significantly associated with high grade, tumour diameter, local invasion and stage, and with high mortality risk by UCLA integrated staging system (UISS) scale. (4) Conclusions: These findings indicate that PRR is associated with the development and progression of renal tumours. Its potential as a novel biomarker for RCC diagnosis/prognosis and as a promising therapeutic target should be taken into account in the future.

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