Partial Nephrectomy For a Presumed Single Renal Mass Revealing Multiple Tumor Histologies: A Series of 4 Patients

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S61-S61
Author(s):  
S Arora ◽  
C G Rogers ◽  
K Arora ◽  
R Abou Shaar ◽  
B Kezlarian ◽  
...  

Abstract Introduction/Objective Renal mass biopsy is known to have a low but unavoidable diagnostic error rate. However, the occurrence of multiple adjacent masses mimicking one mass clinically has been minimally studied. Methods We report a series of four patients who were radiologically presumed to have a single renal mass and treated with partial nephrectomy, yet who were found to have multiple demarcated renal cell carcinoma histologies at pathologic evaluation. Results All were men aged 63–70 years. Grossly, tumors were red brown with scant, bright yellow foci in one of them. Dominant tumors followed by smaller tumors were: patient 1 - clear cell renal cell carcinoma (5.0 cm), clear cell papillary renal cell carcinoma (0.5 cm), and papillary adenoma (0.6 cm); patient 2 - clear cell renal cell carcinoma (1.5 cm) and clear cell papillary renal cell carcinoma (0.5 cm); patient 3 - papillary renal cell carcinoma (5.0 cm) and eosinophilic variant of chromophobe renal cell carcinoma (1.0 cm); patient 4 - chromophobe renal cell carcinoma (4.0 cm) and clear cell papillary renal cell carcinoma (0.6 cm). Immunohistochemical studies for cytokeratin 7, carbonic anhydrase IX, high molecular weight cytokeratin, CD10, and alpha-methyl acyl-CoA racemase (AMACR) confirmed the separate components in all. Conclusion This series adds to the spectrum of causes that may contribute to discordant results of renal mass biopsy and resection specimens. Secondary smaller tumors appear to be predominantly nonaggressive histologies, enriched for clear cell papillary renal cell carcinoma. Pathologists and urologists should be aware of this occurrence when considering the role of renal mass biopsy and interpreting the results.

2021 ◽  
Author(s):  
Sofia Canete-Portillo ◽  
Maria del Carmen Rodriguez Pena ◽  
Dezhi Wang ◽  
Diego F. Sanchez ◽  
George J. Netto ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Jacob W. Bruinius ◽  
Karl J. Dykema ◽  
Sabrina L. Noyes ◽  
Bin Tean Teh ◽  
Brian R. Lane

There is sparse literature demonstrating effective treatments for metastatic chromophobe renal cell carcinoma (ChRCC). The tyrosine kinase inhibitor (TKI) sunitinib selectively inhibits the VEGF pathway and it is a standard care for metastatic clear cell renal cell carcinoma (ccRCC), although data supporting its use in ChRCC is much more limited. A 56-year-old underwent palliative nephrectomy for locally-advanced ChRCC with sarcomatoid differentiation. Tumor gene expression profiling using Affymetrix HG-U133 Plus 2.0 GeneChip platform demonstrated significantly elevated VEGF-C expression compared to normal renal tissue n=12 and other types RCC n=158. Adjuvant sunitinib was used to treat his residual unresectable retroperitoneal lymph nodes. He demonstrated an exceptional response and underwent complete surgical resection four months later. He has been managed with TKIs for nearly nine years with only minimal disease progression. Additional studies exploring treatment options for patients with non-clear cell RCC are needed; in their absence, we would recommend TKIs for patients whose tumors bear a similar molecular profile.


2015 ◽  
Vol 39 (11) ◽  
pp. 1502-1510 ◽  
Author(s):  
Sean R. Williamson ◽  
Nilesh S. Gupta ◽  
John N. Eble ◽  
Craig G. Rogers ◽  
Susan Michalowski ◽  
...  

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 607 ◽  
Author(s):  
José I. López

A multifocal biphasic squamoid alveolar renal cell carcinoma in a 68-year-old man is reported. Four different peripheral tumor nodules were identified on gross examination. A fifth central tumor corresponded to a conventional clear cell renal cell carcinoma. Biphasic squamoid alveolar renal cell carcinoma is a rare tumor that has been very recently characterized as a distinct histotype within the spectrum of papillary renal cell carcinoma. Immunostaining with cyclin D1 seems to be specific of this tumor subtype. This is the first reported case with multifocal presentation.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Mohammed Mohammed ◽  
Liang Cheng ◽  
Dibson Gondim ◽  
...  

AbstractRenal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist’s experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist’s capabilities by providing automated classification for histopathological images.


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