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Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features...

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Autores principales: Mahmood, Hanya, Bradburn, Mike, Rajpoot, Nasir, Islam, Nadim Mohammed, Kujan, Omar, Khurram, Syed Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424112/
https://www.ncbi.nlm.nih.gov/pubmed/35361889
http://dx.doi.org/10.1038/s41379-022-01067-x
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author Mahmood, Hanya
Bradburn, Mike
Rajpoot, Nasir
Islam, Nadim Mohammed
Kujan, Omar
Khurram, Syed Ali
author_facet Mahmood, Hanya
Bradburn, Mike
Rajpoot, Nasir
Islam, Nadim Mohammed
Kujan, Omar
Khurram, Syed Ali
author_sort Mahmood, Hanya
collection PubMed
description Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan–Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p < 0.036) and recurrence (p < 0.015) including bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses and nuclear pleomorphism. This led us to propose two prognostic scoring systems including a ‘6-point model’ using the six features showing a greater statistical association with transformation and recurrence (bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses, nuclear pleomorphism) and a ‘two-point model’ using the two features with highest inter-pathologist agreement (loss of epithelial cohesion and bulbous rete pegs). Both the ‘six point’ and ‘two point’ models showed good predictive ability (AUROC ≥ 0.774 for transformation and 0.726 for recurrence) with further improvement when age, gender and histological grade were added. These results demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and aid patient management. Validation on larger multicentre cohorts with prospective analysis is needed to establish their usefulness in clinical practice.
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spelling pubmed-94241122022-08-31 Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models Mahmood, Hanya Bradburn, Mike Rajpoot, Nasir Islam, Nadim Mohammed Kujan, Omar Khurram, Syed Ali Mod Pathol Article Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan–Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p < 0.036) and recurrence (p < 0.015) including bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses and nuclear pleomorphism. This led us to propose two prognostic scoring systems including a ‘6-point model’ using the six features showing a greater statistical association with transformation and recurrence (bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses, nuclear pleomorphism) and a ‘two-point model’ using the two features with highest inter-pathologist agreement (loss of epithelial cohesion and bulbous rete pegs). Both the ‘six point’ and ‘two point’ models showed good predictive ability (AUROC ≥ 0.774 for transformation and 0.726 for recurrence) with further improvement when age, gender and histological grade were added. These results demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and aid patient management. Validation on larger multicentre cohorts with prospective analysis is needed to establish their usefulness in clinical practice. Nature Publishing Group US 2022-03-31 2022 /pmc/articles/PMC9424112/ /pubmed/35361889 http://dx.doi.org/10.1038/s41379-022-01067-x Text en © Crown 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Mahmood, Hanya
Bradburn, Mike
Rajpoot, Nasir
Islam, Nadim Mohammed
Kujan, Omar
Khurram, Syed Ali
Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title_full Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title_fullStr Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title_full_unstemmed Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title_short Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
title_sort prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424112/
https://www.ncbi.nlm.nih.gov/pubmed/35361889
http://dx.doi.org/10.1038/s41379-022-01067-x
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