Cytopathological quantification of NORs using artificial intelligence to oral cancer screening
Abstract Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. The present study aimed...
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Sociedade Brasileira de Pesquisa Odontológica
2025-05-01
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| Series: | Brazilian Oral Research |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-83242025000101051&lng=en&tlng=en |
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| author | Tatiana Wannmacher LEPPER Luara Nascimento do AMARAL Ana Laura Ferrares ESPINOSA Igor Cavalcante GUEDES Maikel Maciel RÖNNAU Natália Batista DAROIT Alex Nogueira HAAS Fernanda VISIOLI Manuel Menezes de OLIVEIRA NETO Pantelis Varvaki RADOS |
| author_facet | Tatiana Wannmacher LEPPER Luara Nascimento do AMARAL Ana Laura Ferrares ESPINOSA Igor Cavalcante GUEDES Maikel Maciel RÖNNAU Natália Batista DAROIT Alex Nogueira HAAS Fernanda VISIOLI Manuel Menezes de OLIVEIRA NETO Pantelis Varvaki RADOS |
| author_sort | Tatiana Wannmacher LEPPER |
| collection | DOAJ |
| description | Abstract Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. This study included four experimental groups: control, exposure to carcinogens (alcohol and tobacco), oral potentially malignant disorders, and OSCC. In the first phase, 50 cells were used for AgNOR quantification. In the second phase, AgNOR quantification was established in an automated manner using an AgNOR System – Slide Examiner (captured – bounding-boxed – CNN analysis). In phase 1, the cut-off point for considering a smear as suspicious was established at 3.69 AgNORs/nucleus with sensitivity of 86%, specificity of 93%, and accuracy of 90%. In phase 2, the analysis of the intraclass correlation coefficient of AgNORs attributed to the system and human was 0.896 (95% confidence interval = 0.875–0.915; p < 0.0001), and this quantification with the CNN was 20 min compared to 67 h, considering human analysis. The AgNOR Slide-Image Examiner successfully differentiated the nuclei and accurately quantified the number of NORs in oral cytological smears. The cut-off risk point of 3.69 AgNOR/nucleus indicates a suspicious sample may contribute to improvements in oral cancer screening. |
| format | Article |
| id | doaj-art-2bfb9b9363fc4ae09d5cc98c8a8f6667 |
| institution | OA Journals |
| issn | 1807-3107 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Sociedade Brasileira de Pesquisa Odontológica |
| record_format | Article |
| series | Brazilian Oral Research |
| spelling | doaj-art-2bfb9b9363fc4ae09d5cc98c8a8f66672025-08-20T01:54:02ZengSociedade Brasileira de Pesquisa OdontológicaBrazilian Oral Research1807-31072025-05-013910.1590/1807-3107bor-2025.vol39.056Cytopathological quantification of NORs using artificial intelligence to oral cancer screeningTatiana Wannmacher LEPPERhttps://orcid.org/0000-0001-7905-0737Luara Nascimento do AMARALhttps://orcid.org/0000-0001-7767-5047Ana Laura Ferrares ESPINOSAhttps://orcid.org/0000-0003-0891-7043Igor Cavalcante GUEDEShttps://orcid.org/0000-0003-4073-4159Maikel Maciel RÖNNAUhttps://orcid.org/0000-0003-3924-7329Natália Batista DAROIThttps://orcid.org/0000-0002-0764-8999Alex Nogueira HAAShttps://orcid.org/0000-0003-0531-6234Fernanda VISIOLIhttps://orcid.org/0000-0002-4033-8431Manuel Menezes de OLIVEIRA NETOhttps://orcid.org/0000-0003-4957-9984Pantelis Varvaki RADOShttps://orcid.org/0000-0001-9307-1980Abstract Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk assessment measures. The present study aimed to define argyrophilic proteins of the nucleolar organizer region (AgNOR) cut-off risk points by oral exfoliative cytological smears comparing specialized humans with a convolutional neural network (CNN) system AgNOR Slide-Image Examiner. This study included four experimental groups: control, exposure to carcinogens (alcohol and tobacco), oral potentially malignant disorders, and OSCC. In the first phase, 50 cells were used for AgNOR quantification. In the second phase, AgNOR quantification was established in an automated manner using an AgNOR System – Slide Examiner (captured – bounding-boxed – CNN analysis). In phase 1, the cut-off point for considering a smear as suspicious was established at 3.69 AgNORs/nucleus with sensitivity of 86%, specificity of 93%, and accuracy of 90%. In phase 2, the analysis of the intraclass correlation coefficient of AgNORs attributed to the system and human was 0.896 (95% confidence interval = 0.875–0.915; p < 0.0001), and this quantification with the CNN was 20 min compared to 67 h, considering human analysis. The AgNOR Slide-Image Examiner successfully differentiated the nuclei and accurately quantified the number of NORs in oral cytological smears. The cut-off risk point of 3.69 AgNOR/nucleus indicates a suspicious sample may contribute to improvements in oral cancer screening.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-83242025000101051&lng=en&tlng=enMouth NeoplasmsEarly Detection of CancerCytologyArtificial Intelligence |
| spellingShingle | Tatiana Wannmacher LEPPER Luara Nascimento do AMARAL Ana Laura Ferrares ESPINOSA Igor Cavalcante GUEDES Maikel Maciel RÖNNAU Natália Batista DAROIT Alex Nogueira HAAS Fernanda VISIOLI Manuel Menezes de OLIVEIRA NETO Pantelis Varvaki RADOS Cytopathological quantification of NORs using artificial intelligence to oral cancer screening Brazilian Oral Research Mouth Neoplasms Early Detection of Cancer Cytology Artificial Intelligence |
| title | Cytopathological quantification of NORs using artificial intelligence to oral cancer screening |
| title_full | Cytopathological quantification of NORs using artificial intelligence to oral cancer screening |
| title_fullStr | Cytopathological quantification of NORs using artificial intelligence to oral cancer screening |
| title_full_unstemmed | Cytopathological quantification of NORs using artificial intelligence to oral cancer screening |
| title_short | Cytopathological quantification of NORs using artificial intelligence to oral cancer screening |
| title_sort | cytopathological quantification of nors using artificial intelligence to oral cancer screening |
| topic | Mouth Neoplasms Early Detection of Cancer Cytology Artificial Intelligence |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-83242025000101051&lng=en&tlng=en |
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