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http://hdl.handle.net/1843/80498| Tipo: | Artigo de Periódico |
| Título: | Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia |
| Autor(es): | Xinyi Zhang Frederico O. Gleber Netto Shidan Wang Roberta Rayra Martins Chaves Ricardo Santiago Gomez Nadarajah Vigneswaran Arunangshu Sarkar William N. William Jr. Vassiliki Papadimitrakopoulou Michelle Williams Diana Bell Doreen Palsgrove Justin Bishop John V. Heymach Ann M. Gillenwater Jeffrey N. Myers Renata Ferrarotto Scott M. Lippman Curtis Rg Pickering Guanghua Xiao |
| Resumen: | Background: Oral leukoplakia (OL) is associated with an increased risk for oral cancer (OC) development. Prediction of OL cancer progression may contribute to decreased OC morbidity and mortality by favoring early intervention. Current OL progression risk assessment approaches face large interobserver variability and is weakly prognostic. We hypothesized that convolutional neural networks (CNN)-based histology image analyses could accelerate the discovery of better OC progression risk models. Methods: Our CNN-based oral mucosa risk stratification model (OMRS) was trained to classify a set of nondysplastic oral mucosa (OM) and a set of OC H&E slides. As a result, the OMRS model could identify abnormal morphological features of the oral epithelium. By applying this model to OL slides, we hypothesized that the extent of OC-like features identified in the OL epithelium would correlate with its progression risk. The OMRS model scored and categorized the OL cohort (n = 62) into high- and low-risk groups. Results: OL patients classified as high-risk (n = 31) were 3.98 (95% CI 1.36-11.7) times more likely to develop OC than low-risk ones (n = 31). Time-to-progression significantly differed between high- and low-risk groups (p = 0.003). The 5-year OC development probability was 21.3% for low-risk and 52.5% for high-risk patients. The predictive power of the OMRS model was sustained even after adjustment for age, OL site, and OL dysplasia grading (HR = 4.52, 1.5-13.7). Conclusion: The ORMS model successfully identified OL patients with a high risk of OC development and can potentially benefit OC early diagnosis and prevention policies. |
| Asunto: | Carcinogenesis Disease progression Leukoplakia, oral Convolutional neural network Mouth neoplasms |
| Idioma: | eng |
| País: | Brasil |
| Editor: | Universidade Federal de Minas Gerais |
| Sigla da Institución: | UFMG |
| Departamento: | FAO - DEPARTAMENTO DE CLÍNICA |
| Tipo de acceso: | Acesso Aberto |
| Identificador DOI: | https://doi.org/10.1002/cam4.5478 |
| URI: | http://hdl.handle.net/1843/80498 |
| Fecha del documento: | 2023 |
| metadata.dc.url.externa: | https://onlinelibrary.wiley.com/doi/10.1002/cam4.5478 |
| metadata.dc.relation.ispartof: | Cancer Medicine |
| Aparece en las colecciones: | Artigo de Periódico |
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| archivo | Descripción | Tamaño | Formato | |
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| Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia.pdf | 2.28 MB | Adobe PDF | Visualizar/Abrir |
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