Shankargouda Patil1,2, Kamran Habib Awan3, Gururaj Arakeri4, Chaminda Jayampath Seneviratne5, Nagaraj Muddur6, Shuaib Malik7, Marco Ferrari1, Siavash Rahimi8, Peter A Brennan9. 1. Department of Medical Biotechnologies, School of Dental Medicine, University of Siena, Siena, Italy. 2. Division of Oral Pathology, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia. 3. College of Dental Medicine, Roseman University of Health Sciences, South Jordan, Utah. 4. Department of Maxillofacial Surgery, Navodaya Dental College and Hospital, Raichur, Karnataka, India. 5. National Dental Centre, Oral Health ACP, Sing Health Duke, NUS, Singapore City, Singapore. 6. Department of Oral and Maxillofacial Surgery, ESIC Dental College and Hospital, Kalaburagi, Karnataka, India. 7. Department of Oral and Maxillofacial Surgery, John H. Stroger, Jr. Hospital of Cook County, Chicago, Illinois. 8. Department of Histopathology, Queen Alexandra Hospital, Portsmouth, UK. 9. Department of Oral & Maxillofacial Surgery, Queen Alexandra Hospital, Portsmouth, UK.
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.
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.