Machine learning and treatment outcome prediction for oral cancer. (20th August 2020)

• Record Type: Journal Article
• Authors: Chu, Chui S.; Lee, Nikki P.; Adeoye, John; Thomson, Peter; Choi, Siu‐Wai
• Is Part Of: Journal of oral pathology & medicine. Volume 49:Number 10(2020)
• Page Start: 977
• Page End: 985
• Publication Date: 2020-08-20
• Subjects: oral cancer -- oral mucosa; Dentistry -- Periodicals; Teeth -- Diseases -- Periodicals; 617
• DOI: 10.1111/jop.13089
• Languages: English
• ISSNs: 0904-2512

Abstract

Abstract: Background: The natural history of oral squamous cell carcinoma (OSCC) is complicated by progressive disease including loco‐regional tumour recurrence and development of distant metastases. Accurate prediction of tumour behaviour is crucial in delivering individualized treatment plans and developing optimal patient follow‐up and surveillance strategies. Machine learning algorithms may be employed in oncology research to improve clinical outcome prediction. Methods: Retrospective review of 467 OSCC patients treated over a 19‐year period facilitated construction of a detailed clinicopathological database. 34 prognostic features from the database were used to populate 4 machine learning algorithms, linear regression (LR), decision tree (DT), support vector machine (SVM) and k‐nearest neighbours (KNN) models, to attempt progressive disease outcome prediction. Principal component analysis (PCA) and bivariate analysis were used to reduce data dimensionality and highlight correlated variables. Models were validated for accuracy, sensitivity and specificity, with predictive ability assessed by receiver operating characteristic (ROC) and area under the curve (AUC) calculation. Results: Out of 408 fully characterized OSCC patients, 151 (37%) had died and 131 (32%) exhibited progressive disease at the time of data retrieval. The DT model with 34 prognostic features was most successful in identifying "true positive" progressive disease, achieving 70.59% accuracy (AUC 0.67), 41.98% sensitivity and a high specificity of 84.12%. Conclusion: Machine learning models assist clinicians in accessing digitized health information and appear promising in predicting progressive disease outcomes. The future will see increasing emphasis on the use of artificial intelligence to enhance understanding of aggressive tumour behaviour, recurrence and disease progression.