ArticleThe Journal of pathology2023
A digital score of peri-epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia.
Article in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Predicting Malignant Transformation in Oral Epithelial Dysplasia: A Systematic Comparison of Artificial Intelligence-Based Risk Models and Pathologist-Based Microscopy.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026Pooled it
- Pattern-Anchored Diagnosis of Oral Epithelial Dysplasia: A Position Paper From COST Action INTERCEPTOR Working Group 2.Head and neck pathology · 2026Review
- Image-Based Artificial Intelligence for Predicting Malignant Transformation of Oral Potentially Malignant Disorders: A Scoping Review.Journal of clinical medicine · 2026Review
- Dissecting oral premalignant carcinogenesis: spatial omics mechanisms and nanomedicine-driven therapeutic innovation.Molecular cancer · 2026Review
- Biomarker-based machine learning for malignant transformation in oral potentially malignant disorders: a scoping review.BMC oral health · 2026Article
- Artificial intelligence for keratosis characterization and identification of lichenoid lesions in histological samples of oral leukoplakia.Virchows Archiv : an international journal of pathology · 2026Article
- Integrated Clinical, Histopathological, and Inflammatory Parameters for Predicting Malignant Transformation in High-Risk Oral Potentially Malignant Disorders: A Prospective Study.Head and neck pathology · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- Texture-based image feature analysis for the classification of oral squamous cell carcinoma using machine learning approach.BMC oral health · 2025Article
- A bibliometric analysis reveals a dynamic growth in the use of artificial intelligence in oral cancer research over three decades.Discover oncology · 2025Article
- Predicting ustekinumab treatment response in Crohn's disease using pre-treatment biopsy images.Bioinformatics (Oxford, England) · 2025Article
- Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation.Communications medicine · 2025Article
- Multistate oral carcinogenesis-A prospective cohort study and a parallel case-control study in Taiwan.Oral oncology · 2025Article
- A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia.British journal of cancer · 2025Article
- A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.NPJ precision oncology · 2024Article
- An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.Head and neck pathology · 2024Review
- Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.BMC oral health · 2024Article
- New insights into the role of the oral leukoplakia microenvironment in malignant transformation.Frontiers in oral health · 2024Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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