Machine learning and its potential applications to the genomic study of head and neck cancer—A systematic review

Abstract

Background: Machine learning (ML) is powerful tool that can identify and classify patterns from large quantities of cancer genomic data that may lead to the discovery of new biomarkers, new drug targets, and a better understanding of important cancer genes. The aim of this systematic review was to evaluate the existing literature and assess the application of machine learning of genomic data in head and neck cancer (HNC). Materials and methods: The addressed focused question was “Does machine learning of genomic data play a role in prognostic prediction of HNC?” PubMed, EMBASE, Scopus, Web of Science, and gray literature from January 1990 up to and including May 2018 were searched. Two independent reviewers performed the study selection according to eligibility criteria. Results: A total of seven studies that met the eligibility criteria were included. The majority of studies were cohort studies, one a case-control study and one a randomized controlled trial. Two studies each evaluated oral cancer and laryngeal cancer, while other one study each evaluated nasopharyngeal cancer and oropharyngeal cancer. The majority of studies employed support vector machine (SVM) as a ML technique. Among the included studies, the accuracy rates for ML techniques ranged from 56.7% to 99.4%. Conclusion: Our findings showed that ML techniques for the analysis of genomic data can play a role in the prognostic prediction of HNC.