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

Adeoye, John, Koohi-Moghadam, Mohamad, Choi, Siu Wai, Zheng, Li Wu, Lo, Anthony Wing Ip, Tsang, Raymond King Yin, Chow, Velda Ling Yu, Akinshipo, Abdulwarith, Thomson, Peter, and Su, Yu Xiong (2023) Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning. Journal of Big Data, 10 (1). 39.

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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 malignant 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 institutions 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 validation, 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 dysplasia 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 leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings.

Item ID: 78279
Item Type: Article (Research - C1)
ISSN: 2196-1115
Keywords: Artificial intelligence, Machine learning, Oral cancer, Oral leukoplakia, Oral lichen planus, Oral lichenoid lesions, Oral potentially malignant disorders
Copyright Information: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Date Deposited: 30 May 2023 02:40
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3203 Dentistry > 320305 Oral and maxillofacial surgery @ 50%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321109 Predictive and prognostic markers @ 50%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200104 Prevention of human diseases and conditions @ 50%
20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 50%
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