scholarly journals A Deep learning approach for Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images

2019 ◽  
Author(s):  
Sairam Tabibu ◽  
P.K. Vinod ◽  
C.V. Jawahar

ABSTRACTHistopathological images contain morphological markers of disease progression that have diagnostic and predictive values. However, complex morphological information remains unutilized in unaided approach to histopathology. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN’s) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39 % and 87.34 %, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 92.61 %. Here, we introduced a novel support vector machine based method to deal with data imbalance in multi-class classification to improve the accuracy. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

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.


2019 ◽  
Vol 44 (6) ◽  
pp. 2009-2020 ◽  
Author(s):  
Heidi Coy ◽  
Kevin Hsieh ◽  
Willie Wu ◽  
Mahesh B. Nagarajan ◽  
Jonathan R. Young ◽  
...  

2017 ◽  
Vol 19 (2) ◽  
pp. 207-212 ◽  
Author(s):  
Enjing Chen ◽  
Fufu Zheng ◽  
Xiaoxu Yuan ◽  
Yunlin Ye ◽  
Xiaofei Li ◽  
...  

2006 ◽  
Vol 175 (4S) ◽  
pp. 242-243
Author(s):  
W. Scott Webster ◽  
Christine M. Lohse ◽  
R. Houston Thompson ◽  
Haidong Dong ◽  
Xavier Frigola ◽  
...  

Cancer ◽  
2006 ◽  
Vol 107 (1) ◽  
pp. 46-53 ◽  
Author(s):  
W. Scott Webster ◽  
Christine M. Lohse ◽  
R. Houston Thompson ◽  
Haidong Dong ◽  
Xavier Frigola ◽  
...  

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