scholarly journals Clear cell papillary renal cell carcinoma: micro-RNA expression profiling and comparison with clear cell renal cell carcinoma and papillary renal cell carcinoma

2014 ◽  
Vol 45 (6) ◽  
pp. 1130-1138 ◽  
Author(s):  
Enrico Munari ◽  
Luigi Marchionni ◽  
Apurva Chitre ◽  
Masamichi Hayashi ◽  
Guido Martignoni ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Profiling ◽  
Renal Cell ◽  
Clear Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Micro Rna ◽  
Rna Expression ◽  
Cell Renal Cell Carcinoma ◽  
Rna Expression Profiling

SPECIFIC MICRO-RNA EXPRESSION PLAYS A ROLE IN METASTATIC CLEAR CELL RENAL CELL CARCINOMA

2009 ◽  
Vol 181 (4S) ◽  
pp. 33-33
Author(s):  
Joana Heinzelmann ◽  
Jimsgene Sanjmyatav ◽  
Joerg Schubert ◽  
Kerstin Junker
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Micro Rna ◽  
Rna Expression ◽  
Cell Renal Cell Carcinoma

Vascular architectural patterns in clear cell renal cell carcinoma and clear cell papillary renal cell carcinoma

2021 ◽  
Author(s):  
Sofia Canete-Portillo ◽  
Maria del Carmen Rodriguez Pena ◽  
Dezhi Wang ◽  
Diego F. Sanchez ◽  
George J. Netto ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Cell Renal Cell Carcinoma ◽  
Architectural Patterns

Clear Cell Renal Cell Carcinoma With Borderline Features of Clear Cell Papillary Renal Cell Carcinoma

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

scholarly journals Case Report: Multifocal biphasic squamoid alveolar renal cell carcinoma

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 607 ◽  
Author(s):  
José I. López
Keyword(s):  
Renal Cell Carcinoma ◽  
Case Report ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Rare Tumor ◽  
Tumor Subtype ◽  
Cell Renal Cell Carcinoma ◽  
Gross Examination

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.

scholarly journals A pyramidal deep learning pipeline for kidney whole-slide histology images classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Mohammed Mohammed ◽  
Liang Cheng ◽  
Dibson Gondim ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Correct Diagnosis ◽  
Papillary Renal Cell Carcinoma ◽  
Automated Classification ◽  
Cell Renal Cell Carcinoma ◽  
Whole Slide Images

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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