Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning

Aug 4, 2026

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Oral cancer can emerge from oral leukoplakia and oral lichenoid mucositis, both subtypes of oral potentially malignant disorders. Because not every patient progresses to cancer, decision-support tools that predict malignant transformation can enable individualized treatment planning and optimized follow-up. This study compares and selects machine learning models to stratify malignant transformation status in patients with oral leukoplakia and oral lichenoid mucositis.

LanguageEnglish

CategoryResearch & Report

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Pages24

Reading Time1 h 0 min

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Abstract

Introduction

Background of the study

Methods

Adeoye etal. Journal of Big Data (2023) 10:39 [https://doi.org/10.1186/s40537-023-00714-7](https://doi.org/10.1186/s40537-023-00714-7) Journal of Big Data RESEARCH Open Access Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning John Adeoye1, Mohamad Koohi‑Moghadam2, Siu‑Wai Choi3, Li‑Wu Zheng1, Anthony Wing Ip Lo4, Raymond King‑Yin Tsang5, Velda Ling Yu Chow6, Abdulwarith Akinshipo7, Peter Thomson8* andYu‑Xiong Su1* *Correspondence: [peter.thomson1@jcu.edu.au](peter.thomson1@jcu.edu.au); richsu@hku.hk 1 Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, SAR, China 2 Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, SAR, China 3 Department of Orthopedicsand Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China 4 Department of Pathology, Queen Mary Hospital, Hong Kong, SAR, China 5 Division of Otorhinolaryngology, Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China 6 Division of Head and Neck Surgery, Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China 7 Department of Oral and Maxillofacial Pathology and Biology, Faculty of Dental Sciences, University of Lagos, Lagos, Nigeria 8 College of Medicine and Dentistry, James Cook University, Cairns, QLD, Australia Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malig‑ nant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow‑up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)‑based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty‑seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institu‑ tions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment‑based factors obtained at diagnosis and during follow‑up were used to populate and compare forty‑six machine learning‑ based models. These were implemented as a set of twenty‑six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML‑based risk models with twenty‑six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external valida‑ tion, both models achieved a sensitivity, specificity, and F1‑score of 1, 0 . 88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15‑predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dys‑ plasia system for risk stratification (0 . 96 vs 0 . 82) . These findings suggest that machine learning‑based models could be useful potentially to stratify patients with oral leuko‑ plakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Keywords: Artificial intelligence, Machine learning, Oral leukoplakia, Oral lichen planus, Oral lichenoid lesions, Oral potentially malignant disorders, Oral cancer © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4

FAQs

What clinical conditions are targeted for risk prediction in this study?

The study targets patients with oral leukoplakia and oral lichenoid mucositis, including oral lichen planus and oral lichenoid lesions, which are subtypes of oral potentially malignant disorders.

How are the machine learning models constructed and compared?

Demographic, clinical, pathological, and treatment-based factors collected at diagnosis and during follow-up are used to populate and compare 46 machine learning-based models, implemented with different predictor sets depending on data quantity.

What performance is reported for the best models?

The optimal models achieve high accuracy (97% for the 26-predictor model and 94% for the 15-predictor model) and show external validation results including reported sensitivity, specificity, and F1-score on consecutive patients.

Aug 4, 2026

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