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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9495730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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