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Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging

BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis...

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Autores principales: Wu, Qingwu, Chen, Jianning, Ren, Yong, Qiu, Huijun, Yuan, Lianxiong, Deng, Huiyi, Zhang, Yana, Zheng, Rui, Hong, Haiyu, Sun, Yueqi, Wang, Xinyue, Huang, Xuekun, Shao, Chunkui, Lin, Haotian, Han, Lanqing, Yang, Qintai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050855/
https://www.ncbi.nlm.nih.gov/pubmed/33857906
http://dx.doi.org/10.1016/j.ebiom.2021.103336
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author Wu, Qingwu
Chen, Jianning
Ren, Yong
Qiu, Huijun
Yuan, Lianxiong
Deng, Huiyi
Zhang, Yana
Zheng, Rui
Hong, Haiyu
Sun, Yueqi
Wang, Xinyue
Huang, Xuekun
Shao, Chunkui
Lin, Haotian
Han, Lanqing
Yang, Qintai
author_facet Wu, Qingwu
Chen, Jianning
Ren, Yong
Qiu, Huijun
Yuan, Lianxiong
Deng, Huiyi
Zhang, Yana
Zheng, Rui
Hong, Haiyu
Sun, Yueqi
Wang, Xinyue
Huang, Xuekun
Shao, Chunkui
Lin, Haotian
Han, Lanqing
Yang, Qintai
author_sort Wu, Qingwu
collection PubMed
description BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI. METHODS: a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14–70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0. FINDINGS: for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P <0.05), but there were no significant differences in age distribution, onset time, total VAS score, Lund-Kennedy score, or Lund-Mackay score. The percentage of peripheral eosinophils was positively correlated with the percentage of tissue eosinophils (r = 0.560, P <0.001) and negatively correlated with tissue lymphocytes% (r = -0.489, P <0.001), tissue neutrophils% (r = -0.225, P = 0.005), and tissue plasma cells% (r = -0.266, P = 0.001) in WSIs.
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spelling pubmed-80508552021-04-21 Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging Wu, Qingwu Chen, Jianning Ren, Yong Qiu, Huijun Yuan, Lianxiong Deng, Huiyi Zhang, Yana Zheng, Rui Hong, Haiyu Sun, Yueqi Wang, Xinyue Huang, Xuekun Shao, Chunkui Lin, Haotian Han, Lanqing Yang, Qintai EBioMedicine Research Paper BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of inflammatory cells for cellular phenotyping diagnosis of nasal polyps and to explore the clinical significance of different phenotypes of nasal polyps on the WSI. METHODS: a total of 453 patients were enrolled in our study. For the development of AICEP 2.0, 179 patients (WSIs) were obtained from the Third Affiliated Hospital of Sun Yat-Sen University (3HSYSU) from January 2008 to December 2018. A total of 24,625 patches were automatically extracted from the regions of interest under a 400× HPF by Openslide and the number of inflammatory cells in these patches was counted by two pathologists. For the application of AICEP 2.0 in a prospective cohort, 158 patients aged 14–70 years old with chronic rhinosinusitis with nasal polyps (CRSwNP) who had undergone endoscopic sinus surgery at 3HSYSU from June 2020 to December 2020 were included for preoperative demographic characteristics. For the application of AICEP 2.0 in a retrospective cohort, 116 patients with CRSwNP who had undergone endoscopic sinus surgery from May 2016 to June 2017 were enrolled for the recurrence rate. The proportion of inflammatory cells of these patients on WSI was calculated by our AICEP 2.0. FINDINGS: for AICEP 2.0, the mean absolute errors of the ratios of eosinophils, lymphocytes, neutrophils, and plasma cells were 1.64%, 2.13%, 1.06%, and 1.22%, respectively. The four phenotypes of nasal polyps were significantly different in clinical characteristics (including asthma, itching, sneezing, total IgE, peripheral eosinophils%, tissue eosinophils%, tissue neutrophils%, tissue lymphocytes%, tissue plasma cells%, and recurrence rate; P <0.05), but there were no significant differences in age distribution, onset time, total VAS score, Lund-Kennedy score, or Lund-Mackay score. The percentage of peripheral eosinophils was positively correlated with the percentage of tissue eosinophils (r = 0.560, P <0.001) and negatively correlated with tissue lymphocytes% (r = -0.489, P <0.001), tissue neutrophils% (r = -0.225, P = 0.005), and tissue plasma cells% (r = -0.266, P = 0.001) in WSIs. Elsevier 2021-04-12 /pmc/articles/PMC8050855/ /pubmed/33857906 http://dx.doi.org/10.1016/j.ebiom.2021.103336 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Wu, Qingwu
Chen, Jianning
Ren, Yong
Qiu, Huijun
Yuan, Lianxiong
Deng, Huiyi
Zhang, Yana
Zheng, Rui
Hong, Haiyu
Sun, Yueqi
Wang, Xinyue
Huang, Xuekun
Shao, Chunkui
Lin, Haotian
Han, Lanqing
Yang, Qintai
Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title_full Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title_fullStr Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title_full_unstemmed Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title_short Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
title_sort artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050855/
https://www.ncbi.nlm.nih.gov/pubmed/33857906
http://dx.doi.org/10.1016/j.ebiom.2021.103336
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