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

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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Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation

Urology ◽

10.1016/j.urology.2020.05.094 ◽

2020 ◽

Vol 144 ◽

pp. 152-157

Author(s):

Michael Fenstermaker ◽

Scott A. Tomlins ◽

Karandeep Singh ◽

Jenna Wiens ◽

Todd M. Morgan

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Learning Model ◽

Development And Validation ◽

Deep Learning Model

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

Research on Biomedical Engineering ◽

10.1007/s42600-021-00178-9 ◽

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

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A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma

Computational and Structural Biotechnology Journal ◽

10.1016/j.csbj.2020.09.029 ◽

2020 ◽

Vol 18 ◽

pp. 2639-2646

Author(s):

Sugi Lee ◽

Jaeeun Jung ◽

Ilkyu Park ◽

Kunhyang Park ◽

Dae-Soo Kim

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Hierarchical Clustering ◽

Renal Cell ◽

Papillary Renal Cell Carcinoma ◽

Pathological Stage ◽

Clustering Approach

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Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Abdominal Radiology ◽

10.1007/s00261-019-01929-0 ◽

2019 ◽

Vol 44 (6) ◽

pp. 2009-2020 ◽

Cited By ~ 21

Author(s):

Heidi Coy ◽

Kevin Hsieh ◽

Willie Wu ◽

Mahesh B. Nagarajan ◽

Jonathan R. Young ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Cell Renal Cell Carcinoma

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Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning

Frontiers in Oncology ◽

10.3389/fonc.2021.746750 ◽

2021 ◽

Vol 11 ◽

Author(s):

Teng Zuo ◽

Yanhua Zheng ◽

Lingfeng He ◽

Tao Chen ◽

Bin Zheng ◽

...

Keyword(s):

Computed Tomography ◽

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Model Performance ◽

Papillary Renal Cell Carcinoma ◽

Chromophobe Renal Cell Carcinoma ◽

Test Set ◽

Pathological Subtype

ObjectivesThis study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.MethodsTraining and validation datasets were established based on radiological, clinical, and pathological data exported from the radiology, urology, and pathology departments. As the gold standard, reports were reviewed to determine the pathological subtype. Six CNN-based models were trained and validated to differentiate the two subtypes. A special test dataset generated with six new cases and four cases from The Cancer Imaging Archive (TCIA) was applied to validate the efficiency of the best model and of the manual processing by abdominal radiologists. Objective evaluation indexes [accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC)] were calculated to assess model performance.ResultsThe CT image sequences of 70 patients were segmented and validated by two experienced abdominal radiologists. The best model achieved 96.8640% accuracy (99.3794% sensitivity and 94.0271% specificity) in the validation set and 100% (case accuracy) and 93.3333% (image accuracy) in the test set. The manual classification achieved 85% accuracy (100% sensitivity and 70% specificity) in the test set.ConclusionsThis framework demonstrates that DL models could help reliably predict the subtypes of PRCC and ChRCC.

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Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning

BJU International ◽

10.1111/bju.14985 ◽

2020 ◽

Vol 125 (4) ◽

pp. 553-560 ◽

Cited By ~ 5

Author(s):

Amir Baghdadi ◽

Naif A. Aldhaam ◽

Ahmed S. Elsayed ◽

Ahmed A. Hussein ◽

Lora A. Cavuoto ◽

...

Keyword(s):

Computed Tomography ◽

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Chromophobe Renal Cell Carcinoma ◽

Renal Oncocytoma

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03:00 PM Abstract No. 286 Deep learning based on MR imaging for differentiation of low grade and high grade renal cell carcinoma

Journal of Vascular and Interventional Radiology ◽

10.1016/j.jvir.2018.12.351 ◽

2019 ◽

Vol 30 (3) ◽

pp. S128

Author(s):

M. Chang ◽

H. Bai ◽

Y. Zhao ◽

I. Xi ◽

M. Dagli ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Mr Imaging ◽

Cell Carcinoma ◽

Renal Cell ◽

Low Grade ◽

High Grade

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Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma

Scientific Reports ◽

10.1038/s41598-020-80262-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seok-Soo Byun ◽

Tak Sung Heo ◽

Jeong Myeong Choi ◽

Yeong Seok Jeong ◽

Yu Seop Kim ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

External Validation ◽

Training Dataset ◽

Validation Dataset ◽

Cell Renal Cell Carcinoma ◽

Test Dataset

AbstractSurvival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals’ patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel’s C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel’s C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel’s C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.

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