Machine Learning for Predicting Malignant Transformation in Actinic Cheilitis: A Prognostic Support System Based on Demographic and Clinical Descriptors.

Journal: Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Published Date: Jan 19, 2026

Abstract

OBJECTIVE: This study aimed to develop and evaluate Machine Learning models to predict the malignant transformation (MT) in patients with actinic cheilitis (AC). METHODS: Three hundred forty patients diagnosed with AC (322 in the no MT group, and 18 in the MT group) were carefully documented. The study used the Adaptive Synthetic Sampling to adaptively balance the dataset (322 in the no MT group and 319 in the MT group). Four supervised Machine Learning classifiers (Random Forest, Xtreme Gradient Boosting, Multilayer Perceptron, and Support Vector Machine) were trained and tested using 5-fold cross-validation to correlate inputs (clinical descriptors and demographic data) to outputs (MT). SHAP values were used to identify the most influential predictors of MT. RESULTS: The Xtreme Gradient Boosting model stood out, achieving 96.72% accuracy, 96.87% sensitivity, 96.57% specificity, 96.61% precision, 96.73% of F1-Score, and 0.9498 AUC. Multilayer Perceptron showed the best sensitivity (98.44%), and Random Forest presented comparable results. In contrast, Support Vector Machine underperformed, with higher values of false negatives and false positives. Across models, ulceration, multifocality, and long-standing lesions were the strongest predictors of MT, while small, asymptomatic, or solitary lesions were associated with lower risk. CONCLUSION: The results revealed promising performance metrics for Xtreme Gradient Boosting and Multilayer Perceptron suggesting their potential value as tools in a support system for monitoring AC. Additionally, synthetic data proved constructive in training, enhancing the models' robustness and predictive capabilities.

Authors

  • Ivan José Correia-Neto
    Departamento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Alex Franco da Costa
    Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José dos Campos, São Paulo, Brazil.
  • Anna Luíza Damaceno Araújo
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Cristina Saldivia-Siracusa
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Raísa Sales de Sá
    Departamento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Thiago Martini Pereira
    Department of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil.
  • Pablo Agustin Vargas
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Alan Roger Santos-Silva
    Oral Diagnosis Department (Pathology and Semiology), Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.
  • Luiz Paulo Kowalski
    Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil.
  • Matheus Cardoso Moraes
    Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil.
  • Márcio Ajudarte Lopes
    Oral Diagnosis Department (Pathology and Semiology), Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.

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