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Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis

SIMPLE SUMMARY: Papillary thyroid carcinoma is the most common type of thyroid cancer and could be cured if diagnosed and treated early. In clinical practice, the primary method for determining diagnosis of papillary thyroid carcinoma is manual visual inspection of cytopathology slides, which is dif...

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Autores principales: Lin, Yi-Jia, Chao, Tai-Kuang, Khalil, Muhammad-Adil, Lee, Yu-Ching, Hong, Ding-Zhi, Wu, Jia-Jhen, Wang, Ching-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345428/
https://www.ncbi.nlm.nih.gov/pubmed/34359792
http://dx.doi.org/10.3390/cancers13153891
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author Lin, Yi-Jia
Chao, Tai-Kuang
Khalil, Muhammad-Adil
Lee, Yu-Ching
Hong, Ding-Zhi
Wu, Jia-Jhen
Wang, Ching-Wei
author_facet Lin, Yi-Jia
Chao, Tai-Kuang
Khalil, Muhammad-Adil
Lee, Yu-Ching
Hong, Ding-Zhi
Wu, Jia-Jhen
Wang, Ching-Wei
author_sort Lin, Yi-Jia
collection PubMed
description SIMPLE SUMMARY: Papillary thyroid carcinoma is the most common type of thyroid cancer and could be cured if diagnosed and treated early. In clinical practice, the primary method for determining diagnosis of papillary thyroid carcinoma is manual visual inspection of cytopathology slides, which is difficult, time consuming and subjective with a high inter-observer variability and sometimes causes suboptimal patient management due to false-positive and false-negative results. This study presents a fast, fully automatic and efficient deep learning framework for fast screening of cytological slides for thyroid cancer diagnosis. We confirmed the robustness and effectiveness of the proposed method based on evaluation results from two different types of slides: thyroid fine needle aspiration smears and ThinPrep slides. ABSTRACT: Thyroid cancer is the most common cancer in the endocrine system, and papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for 70 to 80% of all thyroid cancer cases. In clinical practice, visual inspection of cytopathological slides is an essential initial method used by the pathologist to diagnose PTC. Manual visual assessment of the whole slide images is difficult, time consuming, and subjective, with a high inter-observer variability, which can sometimes lead to suboptimal patient management due to false-positive and false-negative. In this study, we present a fully automatic, efficient, and fast deep learning framework for fast screening of papanicolaou-stained thyroid fine needle aspiration (FNA) and ThinPrep (TP) cytological slides. To the authors’ best of knowledge, this work is the first study to build an automated deep learning framework for identification of PTC from both FNA and TP slides. The proposed deep learning framework is evaluated on a dataset of 131 WSIs, and the results show that the proposed method achieves an accuracy of 99%, precision of 85%, recall of 94% and F1-score of 87% in segmentation of PTC in FNA slides and an accuracy of 99%, precision of 97%, recall of 98%, F1-score of 98%, and Jaccard-Index of 96% in TP slides. In addition, the proposed method significantly outperforms the two state-of-the-art deep learning methods, i.e., U-Net and SegNet, in terms of accuracy, recall, F1-score, and Jaccard-Index ([Formula: see text]). Furthermore, for run-time analysis, the proposed fast screening method takes 0.4 min to process a WSI and is 7.8 times faster than U-Net and 9.1 times faster than SegNet, respectively.
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spelling pubmed-83454282021-08-07 Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis Lin, Yi-Jia Chao, Tai-Kuang Khalil, Muhammad-Adil Lee, Yu-Ching Hong, Ding-Zhi Wu, Jia-Jhen Wang, Ching-Wei Cancers (Basel) Article SIMPLE SUMMARY: Papillary thyroid carcinoma is the most common type of thyroid cancer and could be cured if diagnosed and treated early. In clinical practice, the primary method for determining diagnosis of papillary thyroid carcinoma is manual visual inspection of cytopathology slides, which is difficult, time consuming and subjective with a high inter-observer variability and sometimes causes suboptimal patient management due to false-positive and false-negative results. This study presents a fast, fully automatic and efficient deep learning framework for fast screening of cytological slides for thyroid cancer diagnosis. We confirmed the robustness and effectiveness of the proposed method based on evaluation results from two different types of slides: thyroid fine needle aspiration smears and ThinPrep slides. ABSTRACT: Thyroid cancer is the most common cancer in the endocrine system, and papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for 70 to 80% of all thyroid cancer cases. In clinical practice, visual inspection of cytopathological slides is an essential initial method used by the pathologist to diagnose PTC. Manual visual assessment of the whole slide images is difficult, time consuming, and subjective, with a high inter-observer variability, which can sometimes lead to suboptimal patient management due to false-positive and false-negative. In this study, we present a fully automatic, efficient, and fast deep learning framework for fast screening of papanicolaou-stained thyroid fine needle aspiration (FNA) and ThinPrep (TP) cytological slides. To the authors’ best of knowledge, this work is the first study to build an automated deep learning framework for identification of PTC from both FNA and TP slides. The proposed deep learning framework is evaluated on a dataset of 131 WSIs, and the results show that the proposed method achieves an accuracy of 99%, precision of 85%, recall of 94% and F1-score of 87% in segmentation of PTC in FNA slides and an accuracy of 99%, precision of 97%, recall of 98%, F1-score of 98%, and Jaccard-Index of 96% in TP slides. In addition, the proposed method significantly outperforms the two state-of-the-art deep learning methods, i.e., U-Net and SegNet, in terms of accuracy, recall, F1-score, and Jaccard-Index ([Formula: see text]). Furthermore, for run-time analysis, the proposed fast screening method takes 0.4 min to process a WSI and is 7.8 times faster than U-Net and 9.1 times faster than SegNet, respectively. MDPI 2021-08-02 /pmc/articles/PMC8345428/ /pubmed/34359792 http://dx.doi.org/10.3390/cancers13153891 Text en © 2021 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
Lin, Yi-Jia
Chao, Tai-Kuang
Khalil, Muhammad-Adil
Lee, Yu-Ching
Hong, Ding-Zhi
Wu, Jia-Jhen
Wang, Ching-Wei
Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title_full Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title_fullStr Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title_full_unstemmed Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title_short Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis
title_sort deep learning fast screening approach on cytological whole slides for thyroid cancer diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345428/
https://www.ncbi.nlm.nih.gov/pubmed/34359792
http://dx.doi.org/10.3390/cancers13153891
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