AUTHOR=Volpe Stefania , Pepa Matteo , Zaffaroni Mattia , Bellerba Federica , Santamaria Riccardo , Marvaso Giulia , Isaksson Lars Johannes , Gandini Sara , Starzyńska Anna , Leonardi Maria Cristina , Orecchia Roberto , Alterio Daniela , Jereczek-Fossa Barbara Alicja 

TITLE=Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist 

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.772663

DOI=10.3389/fonc.2021.772663

ISSN=2234-943X

ABSTRACT=Aim 
To illustrate the potential and limitations of the most commonly used Machine learning (ML) models in solving every-day clinical issues in head and neck cancer (HNC) radiotherapy (RT).
Materials and methods 
Electronic databases were screened up to May 2021. Studies were rated by an adapted version of the qualitative checklist originally developed by Luo et al. Statistical analyses were performed using R version 3.6.1.
Results 
Forty-eight studies on autosegmentation, treatment planning, oncological outcomes and toxicity prediction and on determinants of post-operative RT were included.  No significant differences were identified when works were stratified per their task. 
Conclusion: 
The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young.  Overall, if not safe yet, ML is most probably a bet worth making.