scholarly journals Identifying regions of interest in whole slide images of renal cell carcinoma

2021 ◽  
Author(s):  
Mohammed Lamine Benomar ◽  
Nesma Settouti ◽  
Eric Debreuve ◽  
Xavier Descombes ◽  
Damien Ambrosetti
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Regions Of Interest ◽  
Whole Slide Images

scholarly journals Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

2019 ◽  
Vol 18 ◽  
pp. 153601211988316 ◽  
Author(s):  
Guangjie Yang ◽  
Aidi Gong ◽  
Pei Nie ◽  
Lei Yan ◽  
Wenjie Miao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Texture Features ◽  
Regions Of Interest ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
2D And 3D

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration ( P > .05). There was no significant difference in AUC between the 2 models ( P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.

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.

scholarly journals A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

2021 ◽  
Author(s):  
Jialun Wu ◽  
Ruonan Zhang ◽  
Tieliang Gong ◽  
Xinrui Bao ◽  
Zeyu Gao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Diagnostic Framework ◽  
Whole Slide Images

1253: Different Survival in PT3A Renal Cell Carcinoma with Sinus Fat Invasion or Perinephric Fat Invasion

2007 ◽  
Vol 177 (4S) ◽  
pp. 413-413
Author(s):  
Marco Roscigno ◽  
Roberto Bertini ◽  
Cesare Cozzarini ◽  
Alessandra Pasta ◽  
Mattia Sangalli ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Perinephric Fat

1252: Relative Survival Following Treatment for Renal Cell Carcinoma (RCC)- A S.E.E.R. Analysis: Is Age Important?

2007 ◽  
Vol 177 (4S) ◽  
pp. 413-413
Author(s):  
Yu-Ning Wong ◽  
Brian L. Egleston ◽  
Ismail R. Saad ◽  
Robert G. Uzzo
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Relative Survival

920: Pediatric Renal Cell Carcinoma: An Aggressive Tumor with Biologic Features Distinct From Its Adult Counterpart

2007 ◽  
Vol 177 (4S) ◽  
pp. 305-305
Author(s):  
Richard A. Ashley ◽  
Jonathan C. Routh ◽  
Sameer A. Siddiqui ◽  
Brant A. Inman ◽  
Thomas J. Sebo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Pediatric Renal Cell Carcinoma ◽  
Aggressive Tumor

915: Prognosis and Survival of Synchronous Bilateral Renal Cell Carcinoma: A Multicenter Experience

2007 ◽  
Vol 177 (4S) ◽  
pp. 303-304 ◽  
Author(s):  
Tobias Klatte ◽  
Heiko Wunderlich ◽  
Jean-Jacques Patard ◽  
Mark D. Kleid ◽  
John S. Lam ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Bilateral Renal Cell Carcinoma

908: Preoperative C-Reactive Protein Elevation as a Predictor of Localized Renal Cell Carcinoma

2007 ◽  
Vol 177 (4S) ◽  
pp. 301-301
Author(s):  
Yasumasa Iimura ◽  
Kazutaka Saito ◽  
Minato Yokoyama ◽  
Hitoshi Masuda ◽  
Tsuyoshi Kobayashi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Localized Renal Cell Carcinoma
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