Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist - 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; et al. (Frontiers in Oncology, 2021)
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.