Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Nov 24, 2020; 11(11): 918-934
Published online Nov 24, 2020. doi: 10.5306/wjco.v11.i11.918
Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival
Man Hung, Jungweon Park, Eric S Hon, Jerry Bounsanga, Sara Moazzami, Bianca Ruiz-Negrón, Dawei Wang
Man Hung, Jungweon Park, Sara Moazzami, College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, United States
Man Hung, Department of Orthopaedic Surgery Operations, University of Utah, Salt Lake City, UT 84108, United States
Man Hung, College of Social Work, University of Utah, Salt Lake City, UT 84112, United States
Man Hung, Division of Public Health, University of Utah, Salt Lake City, UT 84108, United States
Man Hung, Department of Educational Psychology, University of Utah, Salt Lake City, UT 84109, United States
Eric S Hon, Department of Economics, University of Chicago, Chicago, IL 60637, United States
Jerry Bounsanga, Research Section, Utah Medical Education Council, Salt Lake City, UT 84102, United States
Bianca Ruiz-Negrón, College of Social and Behavioral Sciences, University of Utah, Salt Lake City, UT 84112, United States
Dawei Wang, Data Analytics Unit, Walmart Inc., Bentonville, AR 72716, United States
Author contributions: Hung M and Hon ES contributed to study conception; Hung M provided study supervision; Hung M and Wang D contributed to research design, data analysis, visualization and results interpretation; Hung M, Hon ES and Bounsanga J contributed to data acquisition; Hung M, Park J, Moazzami S, Ruiz-Negrón B and Wang D contributed to manuscript drafting; Hung M, Park J, Hon ES, Bounsanga J and Wang D contributed to manuscript revision; all authors approved the final version of the manuscript.
Institutional review board statement: This is not a human subject research study. Per the United States federal regulations (45 CFR 46, category 4), this study is deemed exempt and does not require review from Institutional Review Board since the data were deidentified and publicly available.
Informed consent statement: This is not a human subject research study. This study used secondary data that were already collected and were publicly available online. Therefore, signed informed consent form is not relevant.
Conflict-of-interest statement: The authors declare that there is no conflict of interest regarding this work.
Data sharing statement: The data supporting the findings of this study can be accessed at: https://seer.cancer.gov/.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Man Hung, PhD, Professor, Research Dean, College of Dental Medicine, Roseman University of Health Sciences, 10894 S River Front Parkway, South Jordan, UT 84095, United States. mhung@roseman.edu
Received: June 24, 2020
Peer-review started: June 24, 2020
First decision: September 18, 2020
Revised: October 6, 2020
Accepted: October 20, 2020
Article in press: October 20, 2020
Published online: November 24, 2020
Processing time: 147 Days and 10.3 Hours
ARTICLE HIGHLIGHTS
Research background

Oral cancer is highly prevalent in the world, yet there is a limited understanding of oral cancer risk factors and survival.

Research motivation

To increase one’s quality of life, it is important to be able to predict oral cancer survival.

Research objectives

The objectives of this study were to build an accurate model to precisely predict the length of oral cancer survival and to explore the most important factors that determine the longevity of oral cancer survivors.

Research methods

Oral cancer data were obtained from the years 1975 to 2016 in the Surveillance, Epidemiology, and End Results database. Methods from the field of artificial intelligence were applied to build and validate prediction models from 40+ years of oral cancer data representative of the United States’ population.

Research results

Age at diagnosis, primary cancer site, tumor size and year of diagnosis were the most important factors related to oral cancer survival. Individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past, which was a novel finding that had not been reported in the literature.

Research conclusions

Machine learning algorithms were developed this study to predict the length of oral cancer survival that can be readily deployed to clinical settings.

Research perspectives

This study was the first of its kind to use methods from artificial intelligence to examine the length of survival for individuals diagnosed with oral cancer. The outcome of this study has the potential to reduce healthcare disparities and improve the quality of life for oral cancer survivors and their friends and families around the world.