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Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images

Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identif...

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Autores principales: Abu Haeyeh, Yasmine, Ghazal, Mohammed, El-Baz, Ayman, Talaat, Iman M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495730/
https://www.ncbi.nlm.nih.gov/pubmed/36134972
http://dx.doi.org/10.3390/bioengineering9090423
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author Abu Haeyeh, Yasmine
Ghazal, Mohammed
El-Baz, Ayman
Talaat, Iman M.
author_facet Abu Haeyeh, Yasmine
Ghazal, Mohammed
El-Baz, Ayman
Talaat, Iman M.
author_sort Abu Haeyeh, Yasmine
collection PubMed
description Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model.
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spelling pubmed-94957302022-09-23 Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images Abu Haeyeh, Yasmine Ghazal, Mohammed El-Baz, Ayman Talaat, Iman M. Bioengineering (Basel) Article Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model. MDPI 2022-08-30 /pmc/articles/PMC9495730/ /pubmed/36134972 http://dx.doi.org/10.3390/bioengineering9090423 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abu Haeyeh, Yasmine
Ghazal, Mohammed
El-Baz, Ayman
Talaat, Iman M.
Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title_full Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title_fullStr Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title_full_unstemmed Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title_short Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images
title_sort development and evaluation of a novel deep-learning-based framework for the classification of renal histopathology images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9495730/
https://www.ncbi.nlm.nih.gov/pubmed/36134972
http://dx.doi.org/10.3390/bioengineering9090423
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