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Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold

BACKGROUND: Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. METHODS: Low‐grade squamou...

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Autores principales: Yao, Bin, Feng, Yadong, Zhao, Kai, Liang, Yan, Huang, Peilin, Zang, Juncai, Song, Jie, Li, Mengjie, Wang, Xiaofen, Shu, Huazhong, Shi, Ruihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883535/
https://www.ncbi.nlm.nih.gov/pubmed/35766144
http://dx.doi.org/10.1002/cam4.4984
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author Yao, Bin
Feng, Yadong
Zhao, Kai
Liang, Yan
Huang, Peilin
Zang, Juncai
Song, Jie
Li, Mengjie
Wang, Xiaofen
Shu, Huazhong
Shi, Ruihua
author_facet Yao, Bin
Feng, Yadong
Zhao, Kai
Liang, Yan
Huang, Peilin
Zang, Juncai
Song, Jie
Li, Mengjie
Wang, Xiaofen
Shu, Huazhong
Shi, Ruihua
author_sort Yao, Bin
collection PubMed
description BACKGROUND: Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. METHODS: Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. RESULTS: AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10(−76)) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10(−84)). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10(−14)), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10(−15)), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10(−14)). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10(−58)), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10(−)8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10(−58)), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10(−8)) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. CONCLUSION: Our study provides a novel cytological method for detecting and screening early ESCC and HGIN.
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spelling pubmed-98835352023-01-31 Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold Yao, Bin Feng, Yadong Zhao, Kai Liang, Yan Huang, Peilin Zang, Juncai Song, Jie Li, Mengjie Wang, Xiaofen Shu, Huazhong Shi, Ruihua Cancer Med RESEARCH ARTICLES BACKGROUND: Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. METHODS: Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. RESULTS: AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10(−76)) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10(−84)). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10(−14)), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10(−15)), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10(−14)). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10(−58)), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10(−)8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10(−58)), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10(−8)) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. CONCLUSION: Our study provides a novel cytological method for detecting and screening early ESCC and HGIN. John Wiley and Sons Inc. 2022-06-29 /pmc/articles/PMC9883535/ /pubmed/35766144 http://dx.doi.org/10.1002/cam4.4984 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESEARCH ARTICLES
Yao, Bin
Feng, Yadong
Zhao, Kai
Liang, Yan
Huang, Peilin
Zang, Juncai
Song, Jie
Li, Mengjie
Wang, Xiaofen
Shu, Huazhong
Shi, Ruihua
Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title_full Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title_fullStr Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title_full_unstemmed Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title_short Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
title_sort artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold
topic RESEARCH ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883535/
https://www.ncbi.nlm.nih.gov/pubmed/35766144
http://dx.doi.org/10.1002/cam4.4984
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