Comparison of Prognosis in Types 1 and 2 Papillary Renal Cell Carcinoma and Clear Cell
 Renal Cell Carcinoma in T1 Stage

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.

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

Scientific Reports ◽

10.1038/s41598-021-99735-6 ◽

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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Similarities and Differences between Clear Cell Tubulo-Papillary and Conventional Clear Cell Renal Cell Carcinoma: A Comparative Phenotypical and Mutational Analysis

Diagnostics ◽

10.3390/diagnostics10020123 ◽

2020 ◽

Vol 10 (2) ◽

pp. 123

Author(s):

Francesca Giunchi ◽

Tania Franceschini ◽

Elisa Gruppioni ◽

Annalisa Altimari ◽

Elisa Capizzi ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Mutational Analysis ◽

Papillary Renal Cell Carcinoma ◽

Tumor Type ◽

Wild Type ◽

Cell Renal Cell Carcinoma

Background: Clear cell tubulo-papillary renal cell carcinoma (cctpRCC) is characterized by clear cell morphology, but differs from conventional clear cell carcinoma (ccRCC) for its indolent clinical behavior and genetic background. The differential diagnosis between the two is based on histology and immunohistochemistry (IHC). Methods: We performed a comparative case-control histological, IHC, and genetic analysis by next generation sequencing (NGS), to point out the differences in 10 cases of cctpRCC, and six controls of ccRCC with low stage and grade. Results: All 16 cases showed the IHC profile with cytokeratin 7, racemase, and carbonic anhydrase IX expected for the histological features of each tumor type. By contrast, the NGS mutation analysis that covered 207 amplicons of 50 oncogenes or tumor suppressor genes provided conflicting results. Among the 10 cctpRCC cases, eight (80%) were wild type for all of the genes in the panel, while two (20%) harbored VHL mutations typical of ccRCC. Three of the six (50%) ccRCC control cases showed expected VHL mutations; two (33%) harbored pathogenic mutations in the p53 or the CKIT genes; and one (16%) was wild type. Conclusion: We can assume that histology and ICH are not sufficient for a definitive diagnosis of cctpRCC or ccRCC. Although with a panel covering 50 genes, we found that 80% of cctpRCC were genetically silent; thus, suggesting an indolent biology of these tumors. The differential diagnosis between ccptRCC and ccRCC for the choice of the best therapeutic strategy likely requires the comprehensive evaluation of histology, IHC, and at least VHL mutations.

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