Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma

• Main Authors: Alkhadar, H. (Author), Ellis, I. (Author), Gardner, A. (Author), Macluskey, M. (Author), White, S. (Author)
• Format: Article
• Language: English
• Published: Blackwell Publishing Ltd 2021
• Subjects: algorithm; Algorithms; Article; Bayes theorem; Bayes Theorem; bone metastasis; cancer staging; Carcinoma, Squamous Cell; controlled study; decision tree; female; Head and Neck Neoplasms; head and neck tumor; histopathology; human; Humans; lymph vessel; machine learning; Machine Learning; major clinical study; male; Mouth Neoplasms; mouth squamous cell carcinoma; mouth tumor; oral cancer; oral mucosa; perineural invasion; predictive value; priority journal; recurrence free survival; Retrospective Studies; retrospective study; sensitivity and specificity; squamous cell carcinoma; Squamous Cell Carcinoma of Head and Neck; task performance
• Online Access: View Fulltext in Publisher

 Background/Aim: Machine learning analyses of cancer outcomes for oral cancer remain sparse compared to other types of cancer like breast or lung. The purpose of the present study was to compare the performance of machine learning algorithms in the prediction of global, recurrence-free five-year survival in oral cancer patients based on clinical and histopathological data. Methods: Data were gathered retrospectively from 416 patients with oral squamous cell carcinoma. The data set was divided into training and test data set (75:25 split). Training performance of five machine learning algorithms (Logistic regression, K-nearest neighbours, Naïve Bayes, Decision tree and Random forest classifiers) for prediction was assessed by k-fold cross-validation. Variables used in the machine learning models were age, sex, pain symptoms, grade of lesion, lymphovascular invasion, extracapsular extension, perineural invasion, bone invasion and type of treatment. Variable importance was assessed and model performance on the testing data was assessed using receiver operating characteristic curves, accuracy, sensitivity, specificity and F1 score. Results: The best performing model was the Decision tree classifier, followed by the Logistic Regression model (accuracy 76% and 60%, respectively). The Naïve Bayes model did not display any predictive value with 0% specificity. Conclusions: Machine learning presents a promising and accessible toolset for improving prediction of oral cancer outcomes. Our findings add to a growing body of evidence that Decision tree models are useful in models in predicting OSCC outcomes. We would advise that future similar studies explore a variety of machine learning models including Logistic regression to help evaluate model performance. © 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd