Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review

Abstract

INTRODUCTION: Despite multiple prognostic indicators described for oral cavity squamous cell carcinoma (OCSCC), its management still continues to be a matter of debate. Machine learning is a subset of artificial intelligence that enables computers to learn from historical data, gather insights, and make predictions about new data using the model learned. Therefore, it can be a potential tool in the field of head and neck cancer. METHODS: We conducted a systematic review. RESULTS: A total of 81 manuscripts were revised, and 46 studies met the inclusion criteria. Of these, 38 were excluded for the following reasons: use of a classical statistical method (N = 16), nonspecific for OCSCC (N = 15), and not being related to OCSCC survival (N = 7). In total, 8 studies were included in the final analysis. CONCLUSIONS: ML has the potential to significantly advance research in the field of OCSCC. Advantages are related to the use and training of ML models because of their capability to continue training continuously when more data become available. Future ML research will allow us to improve and democratize the application of algorithms to improve the prediction of cancer prognosis and its management worldwide.

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