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Clinical-grade endometrial cancer detection system via whole-slide images using deep learning
The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668742/ https://www.ncbi.nlm.nih.gov/pubmed/36408137 http://dx.doi.org/10.3389/fonc.2022.1040238 |
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author | Zhang, Xiaobo Ba, Wei Zhao, Xiaoya Wang, Chen Li, Qiting Zhang, Yinli Lu, Shanshan Wang, Lang Wang, Shuhao Song, Zhigang Shen, Danhua |
author_facet | Zhang, Xiaobo Ba, Wei Zhao, Xiaoya Wang, Chen Li, Qiting Zhang, Yinli Lu, Shanshan Wang, Lang Wang, Shuhao Song, Zhigang Shen, Danhua |
author_sort | Zhang, Xiaobo |
collection | PubMed |
description | The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists’ confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists. |
format | Online Article Text |
id | pubmed-9668742 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96687422022-11-18 Clinical-grade endometrial cancer detection system via whole-slide images using deep learning Zhang, Xiaobo Ba, Wei Zhao, Xiaoya Wang, Chen Li, Qiting Zhang, Yinli Lu, Shanshan Wang, Lang Wang, Shuhao Song, Zhigang Shen, Danhua Front Oncol Oncology The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists’ confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists. Frontiers Media S.A. 2022-11-02 /pmc/articles/PMC9668742/ /pubmed/36408137 http://dx.doi.org/10.3389/fonc.2022.1040238 Text en Copyright © 2022 Zhang, Ba, Zhao, Wang, Li, Zhang, Lu, Wang, Wang, Song and Shen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Zhang, Xiaobo Ba, Wei Zhao, Xiaoya Wang, Chen Li, Qiting Zhang, Yinli Lu, Shanshan Wang, Lang Wang, Shuhao Song, Zhigang Shen, Danhua Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title | Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title_full | Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title_fullStr | Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title_full_unstemmed | Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title_short | Clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
title_sort | clinical-grade endometrial cancer detection system via whole-slide images using deep learning |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668742/ https://www.ncbi.nlm.nih.gov/pubmed/36408137 http://dx.doi.org/10.3389/fonc.2022.1040238 |
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