A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia

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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 a novel artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide image (WSI). 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). The proposed OMTscore achieved an AUROC ≥ 0.75 in predicting OED progression. Survival analyses showed the prognostic value of our OMTscore, compared to WHO and binary grades. Nuclear analyses elucidated the presence of peri-epithelial and epithelium-infiltrating lymphocytes in highly predictive patches of transforming cases (p < 0.0001). This is the first study to propose a completely automated, interpretable and externally validated algorithm for predicting OED transformation. Our algorithm shows better-than-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.