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Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease
Extramammary Paget’s disease (EMPD) is a rare, malignant cutaneous adenocarcinoma with a high recurrence rate after surgical resection. Early diagnosis of EMPD is critical as 15%–40% of cases progress into an invasive form and resulting in a dismal prognosis. However, EMPD can be a diagnostic challe...
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/PMC8804211/ https://www.ncbi.nlm.nih.gov/pubmed/35118000 http://dx.doi.org/10.3389/fonc.2021.810909 |
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author | Wu, Hao Chen, Huyan Wang, Xuchao Yu, Liheng Yu, Zekuan Shi, Zhijie Xu, Jinhua Dong, Biqin Zhu, Shujin |
author_facet | Wu, Hao Chen, Huyan Wang, Xuchao Yu, Liheng Yu, Zekuan Shi, Zhijie Xu, Jinhua Dong, Biqin Zhu, Shujin |
author_sort | Wu, Hao |
collection | PubMed |
description | Extramammary Paget’s disease (EMPD) is a rare, malignant cutaneous adenocarcinoma with a high recurrence rate after surgical resection. Early diagnosis of EMPD is critical as 15%–40% of cases progress into an invasive form and resulting in a dismal prognosis. However, EMPD can be a diagnostic challenge to pathologists, especially in the grassroots hospital, because of its low incidence and nonspecific clinical presentation. Although AI-enabled computer-aided diagnosis solutions have been extensively used in dermatological pathological image analysis to diagnose common skin cancers such as melanoma and basal cell carcinoma, these techniques have yet been applied to diagnose EMPD. Here, we developed and verified a deep learning method with five different deep convolutional neural networks, named ResNet34, ResNet50, MobileNetV2, GoogLeNet, and VGG16, in Asian EMPD pathological image screening to distinguish between Paget’s and normal cells. We further demonstrated that the results of the proposed method are quantitative, fast, and repeatable by a retrospective single-center study. The ResNet34 model achieved the best performance with an accuracy of 95.522% in pathological images collected at a magnification of ×40. We envision this method can potentially empower grassroots pathologists’ efficiency and accuracy as well as to ultimately provide better patient care. |
format | Online Article Text |
id | pubmed-8804211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88042112022-02-02 Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease Wu, Hao Chen, Huyan Wang, Xuchao Yu, Liheng Yu, Zekuan Shi, Zhijie Xu, Jinhua Dong, Biqin Zhu, Shujin Front Oncol Oncology Extramammary Paget’s disease (EMPD) is a rare, malignant cutaneous adenocarcinoma with a high recurrence rate after surgical resection. Early diagnosis of EMPD is critical as 15%–40% of cases progress into an invasive form and resulting in a dismal prognosis. However, EMPD can be a diagnostic challenge to pathologists, especially in the grassroots hospital, because of its low incidence and nonspecific clinical presentation. Although AI-enabled computer-aided diagnosis solutions have been extensively used in dermatological pathological image analysis to diagnose common skin cancers such as melanoma and basal cell carcinoma, these techniques have yet been applied to diagnose EMPD. Here, we developed and verified a deep learning method with five different deep convolutional neural networks, named ResNet34, ResNet50, MobileNetV2, GoogLeNet, and VGG16, in Asian EMPD pathological image screening to distinguish between Paget’s and normal cells. We further demonstrated that the results of the proposed method are quantitative, fast, and repeatable by a retrospective single-center study. The ResNet34 model achieved the best performance with an accuracy of 95.522% in pathological images collected at a magnification of ×40. We envision this method can potentially empower grassroots pathologists’ efficiency and accuracy as well as to ultimately provide better patient care. Frontiers Media S.A. 2022-01-18 /pmc/articles/PMC8804211/ /pubmed/35118000 http://dx.doi.org/10.3389/fonc.2021.810909 Text en Copyright © 2022 Wu, Chen, Wang, Yu, Yu, Shi, Xu, Dong and Zhu 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 Wu, Hao Chen, Huyan Wang, Xuchao Yu, Liheng Yu, Zekuan Shi, Zhijie Xu, Jinhua Dong, Biqin Zhu, Shujin Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title | Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title_full | Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title_fullStr | Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title_full_unstemmed | Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title_short | Development and Validation of an Artificial Intelligence-Based Image Classification Method for Pathological Diagnosis in Patients With Extramammary Paget’s Disease |
title_sort | development and validation of an artificial intelligence-based image classification method for pathological diagnosis in patients with extramammary paget’s disease |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804211/ https://www.ncbi.nlm.nih.gov/pubmed/35118000 http://dx.doi.org/10.3389/fonc.2021.810909 |
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