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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9883535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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