ArticleNPJ precision oncology2024
A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Biomarker-based machine learning for malignant transformation in oral potentially malignant disorders: a scoping review.BMC oral health · 2026Article
- Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.International journal of clinical oncology · 2026Review
- Independent Multicentre Validation of the 'Six-Point' Model for Malignant Transformation Risk in Oral Epithelial Dysplasia.Oral diseases · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- 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
- Artificial Intelligence Basics for Head and Neck Pathologists: A Simple Guide.Head and neck pathology · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- A spatial-microbial-metabolic-immune framework for high-risk oral potentially malignant disorders: an evidence-ranked review for malignant transformation risk stratification.Frontiers in cellular and infection microbiology · 2026Review
- Research progress on carcinogenic factors and personalized treatment of oral cancer.Discover oncology · 2025Review
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 2025Article
- Oral leukoplakia: still an enigmatic disorder.Medicina oral, patologia oral y cirugia bucal · 2025Review
- The Limitations of Artificial Intelligence in Head and Neck Oncology.Advances in therapy · 2025Review
- 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
- The tumour histopathology "glossary" for AI developers.PLoS computational biology · 2025Review
- Integrated multicenter deep learning system for prognostic prediction in bladder cancer.NPJ precision oncology · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.