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

Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review...

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Bibliographic Details
Published in:Frontiers in oncology 2021-11, Vol.11, p.772663
Main Authors: 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
Format: Article
Language:English
Subjects:
artificial intelligence
head and neck cancer
machine learning
Oncology
radiotherapy
systematic review
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Summary:Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). 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.
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2021.772663