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A digital quantitative analysis was performed on 75 OED whole-slide images to build multivariable models using epithelial cellularity, nuclear geometry, cytoplasm staining intensity, and epithelial architecture/thickness. The strongest models were externally validated on 121 pooled cases, achieving higher predictive performance for malignant transformation and OED recurrence than WHO grading.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-and-validation-of-a-multivariable-model-for-prediction-of-malignant-transformation-and-recurrence-of-oral-epithelial-dysplasia/377373/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-and-validation-of-a-multivariable-model-for-prediction-of-malignant-transformation-and-recurrence-of-oral-epithelial-dysplasia/377373.png","ImageObject",300,407,{"name":92,"@type":93},"Aldword","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of the study on oral epithelial dysplasia (OED)?","Question",{"text":112,"@type":113},"The study develops and validates a multivariable prognostic model to predict OED malignant transformation and recurrence, addressing limitations of conventional histological grading.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and quantitative features were used to build the model?",{"text":117,"@type":113},"The model used digital quantitative measurements of epithelial cellularity, nuclear geometry, cytoplasm staining intensity, and epithelial architecture/thickness from OED whole-slide images.",{"name":119,"@type":110,"acceptedAnswer":120},"How were the models evaluated and what did external validation show?",{"text":121,"@type":113},"Models were validated on unseen pooled cases from four centres, and the developed approach showed superior predictive potential for malignant transformation and OED recurrence compared with WHO grading.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},377373,1790618467,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},2336478940917,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","British Journal of Cancer [www.nature.com/bjc](www.nature.com/bjc)  \nARTICLE OPEN   \nDevelopment and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia  \nHanya Mahmood 1 ✉ , Adam Shephard2, Paul Hankinson3, Mike Bradburn4, Anna Luiza Damaceno Araujo5, Alan Roger Santos-Silva5, Marcio Ajudarte Lopes5, Pablo Agustin Vargas5, Kris D. McCombe6, Stephanie G. Craig6, Jacqueline James 6, Jill Brooks7,  \nPaul Nankivell 7, Hisham Mehanna7, Nasir Rajpoot 2,8 and Syed Ali Khurram3,8  \n© The Author(s) 2023  \n\n|  | BACKGROUND: Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma which is amongst the top ten cancers worldwide. Prognostic significance of conventional histological features in OED is not well established. Many additional histological abnormalities are seen in OED, but are insufficiently investigated, and have not been correlated to clinical outcomes. METHODS: A digital quantitative analysis of epithelial cellularity, nuclear geometry, cytoplasm staining intensity and epithelial architecture/thickness is conducted on 75 OED whole-slide images (252 regions of interest) with feature-specific comparisons between grades and against non-dysplastic/control cases. Multivariable models were developed to evaluate prediction of OED recurrence and malignant transformation. The best performing models were externally validated on unseen cases pooled from four different centres (n = 121), of which 32% progressed to cancer, with an average transformation time of 45 months.\u003Cbr>RESULTS: Grade-based differences were seen for cytoplasmic eosin, nuclear eccentricity, and circularity in basal epithelial cells of OED (p \u003C 0.05) . Nucleus circularity was associated with OED recurrence (p = 0.018) and epithelial perimeter associated with malignant transformation (p = 0.03) . The developed model demonstrated superior predictive potential for malignant transformation (AUROC 0.77) and OED recurrence (AUROC 0.74) as compared with conventional WHO grading (AUROC 0.68 and 0.71, respectively) . External validation supported the prognostic strength of this model.\u003Cbr>CONCLUSIONS: This study supports a novel prognostic model which outperforms existing grading systems Further studies are |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| .\u003Cbr>warranted to evaluate its significance for OED prognostication. |  |  |\n|  | British Journal of Cancer (2023) 129:1599–1607; [https://doi.org/10.1038/s41416-023-02438-0](https://doi.org/10.1038/s41416-023-02438-0) |  |\n|  |  |  |\n\nBACKGROUND  \nOral epithelial dysplasia (OED) is a ‘pre-cancerous state’ histologically characterised by cellular atypia with loss of normal maturation and stratification of stratified squamous epithelium [1, 2] . Its progression to malignancy (oral squamous cell carcinoma or OSCC) is a progressive multi-step process which can be initiated by chemical carcinogen exposure (such as tobacco) [3], genetic mutations [4–6] and in a small subset of cases, by high-risk human papilloma virus (HPV) [7] . The progression of OED to OSCC is variable (mild OED 1.7%; severe OED 3.57%, annually) [8, 9] and difficult to predict due to poor understanding of the disease pathway [10, 11] .  \nConventionally, a diagnosis of OED is reached following identification of a wide range of histological architectural (whole epithelium) and cytological (individual keratinocyte) abnormalities using the World Health Organisation (WHO) criteria [12] . This  \nthree-tier grading system (mild, moderate, severe) was recently updated from the fifth edition of the WHO classification to further expand the range of diagnostic features to twenty-seven in total (14) . However, the prognostic strength of these features remains poorly understood [13, 11] and, individually, many of them are relatively non-specific [11] and evident in a host ","cbCaieQGFwT5Kxqz","https://ap.wps.com/l/cbCaieQGFwT5Kxqz","pdf",1383670,"English","# Background\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What is the purpose of the study on oral epithelial dysplasia (OED)?\",\"answer\":\"The study develops and validates a multivariable prognostic model to predict OED malignant transformation and recurrence, addressing limitations of conventional histological grading.\"},{\"question\":\"What data and quantitative features were used to build the model?\",\"answer\":\"The model used digital quantitative measurements of epithelial cellularity, nuclear geometry, cytoplasm staining intensity, and epithelial architecture/thickness from OED whole-slide images.\"},{\"question\":\"How were the models evaluated and what did external validation show?\",\"answer\":\"Models were validated on unseen pooled cases from four centres, and the developed approach showed superior predictive potential for malignant transformation and OED recurrence compared with WHO grading.\"}]","Development and validation of a multivariable model for prediction of malignant transformation and recurrence of oral epithelial dysplasia | PDF",1790224671,23]