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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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. |
doi_str_mv | 10.1038/s41379-022-01067-x |
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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.</description><identifier>ISSN: 0893-3952</identifier><identifier>EISSN: 1530-0285</identifier><identifier>DOI: 10.1038/s41379-022-01067-x</identifier><identifier>PMID: 35361889</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>631/67/1536/1665 ; 692/53/2422 ; Agreements ; Archives & records ; Biopsy ; Carcinoma, Squamous Cell - pathology ; Cell Transformation, Neoplastic - pathology ; Clinical outcomes ; Data collection ; Dentistry ; Dyskeratosis ; Dysplasia ; Genetic transformation ; Head and Neck Neoplasms ; Humans ; Hyperplasia ; Keratin ; Laboratory Medicine ; Medicine ; Medicine & Public Health ; Mitosis ; Mouth Neoplasms - pathology ; Oral carcinoma ; Oral squamous cell carcinoma ; Pathology ; Pleomorphism ; Precancerous Conditions - pathology ; Predictions ; Prognosis ; Squamous cell carcinoma</subject><ispartof>Modern pathology, 2022-09, Vol.35 (9), p.1151-1159</ispartof><rights>2022 The Author(s)</rights><rights>Crown 2022</rights><rights>2022. Crown.</rights><rights>Crown 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c527t-b91721a5c218b16bcd7eb4c60ab7f041ddc185af7bcf4748faf37ab89c160513</citedby><cites>FETCH-LOGICAL-c527t-b91721a5c218b16bcd7eb4c60ab7f041ddc185af7bcf4748faf37ab89c160513</cites><orcidid>0000-0001-6760-1271 ; 0000-0002-5951-8280 ; 0000-0001-7159-0368</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2707724110?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,64385,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35361889$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mahmood, Hanya</creatorcontrib><creatorcontrib>Bradburn, Mike</creatorcontrib><creatorcontrib>Rajpoot, Nasir</creatorcontrib><creatorcontrib>Islam, Nadim Mohammed</creatorcontrib><creatorcontrib>Kujan, Omar</creatorcontrib><creatorcontrib>Khurram, Syed Ali</creatorcontrib><title>Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models</title><title>Modern pathology</title><addtitle>Mod Pathol</addtitle><addtitle>Mod Pathol</addtitle><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.</description><subject>631/67/1536/1665</subject><subject>692/53/2422</subject><subject>Agreements</subject><subject>Archives & records</subject><subject>Biopsy</subject><subject>Carcinoma, Squamous Cell - pathology</subject><subject>Cell Transformation, Neoplastic - pathology</subject><subject>Clinical outcomes</subject><subject>Data collection</subject><subject>Dentistry</subject><subject>Dyskeratosis</subject><subject>Dysplasia</subject><subject>Genetic transformation</subject><subject>Head and Neck Neoplasms</subject><subject>Humans</subject><subject>Hyperplasia</subject><subject>Keratin</subject><subject>Laboratory Medicine</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mitosis</subject><subject>Mouth Neoplasms - pathology</subject><subject>Oral carcinoma</subject><subject>Oral squamous cell carcinoma</subject><subject>Pathology</subject><subject>Pleomorphism</subject><subject>Precancerous Conditions - 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pathology</topic><topic>Cell Transformation, Neoplastic - pathology</topic><topic>Clinical outcomes</topic><topic>Data collection</topic><topic>Dentistry</topic><topic>Dyskeratosis</topic><topic>Dysplasia</topic><topic>Genetic transformation</topic><topic>Head and Neck Neoplasms</topic><topic>Humans</topic><topic>Hyperplasia</topic><topic>Keratin</topic><topic>Laboratory Medicine</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mitosis</topic><topic>Mouth Neoplasms - pathology</topic><topic>Oral carcinoma</topic><topic>Oral squamous cell carcinoma</topic><topic>Pathology</topic><topic>Pleomorphism</topic><topic>Precancerous Conditions - pathology</topic><topic>Predictions</topic><topic>Prognosis</topic><topic>Squamous cell carcinoma</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mahmood, Hanya</creatorcontrib><creatorcontrib>Bradburn, Mike</creatorcontrib><creatorcontrib>Rajpoot, Nasir</creatorcontrib><creatorcontrib>Islam, Nadim Mohammed</creatorcontrib><creatorcontrib>Kujan, Omar</creatorcontrib><creatorcontrib>Khurram, Syed Ali</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Modern pathology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mahmood, Hanya</au><au>Bradburn, Mike</au><au>Rajpoot, Nasir</au><au>Islam, Nadim Mohammed</au><au>Kujan, Omar</au><au>Khurram, Syed Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models</atitle><jtitle>Modern pathology</jtitle><stitle>Mod Pathol</stitle><addtitle>Mod Pathol</addtitle><date>2022-09-01</date><risdate>2022</risdate><volume>35</volume><issue>9</issue><spage>1151</spage><epage>1159</epage><pages>1151-1159</pages><issn>0893-3952</issn><eissn>1530-0285</eissn><abstract>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.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><pmid>35361889</pmid><doi>10.1038/s41379-022-01067-x</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6760-1271</orcidid><orcidid>https://orcid.org/0000-0002-5951-8280</orcidid><orcidid>https://orcid.org/0000-0001-7159-0368</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 631/67/1536/1665 692/53/2422 Agreements Archives & records Biopsy Carcinoma, Squamous Cell - pathology Cell Transformation, Neoplastic - pathology Clinical outcomes Data collection Dentistry Dyskeratosis Dysplasia Genetic transformation Head and Neck Neoplasms Humans Hyperplasia Keratin Laboratory Medicine Medicine Medicine & Public Health Mitosis Mouth Neoplasms - pathology Oral carcinoma Oral squamous cell carcinoma Pathology Pleomorphism Precancerous Conditions - pathology Predictions Prognosis Squamous cell carcinoma |
title | Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models |
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