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

Background and PurposeMachine 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 s...

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Autores principales:	Stefania Volpe, Matteo Pepa, Mattia Zaffaroni, Federica Bellerba, Riccardo Santamaria, Giulia Marvaso, Lars Johannes Isaksson, Sara Gandini, Anna Starzyńska, Maria Cristina Leonardi, Roberto Orecchia, Daniela Alterio, Barbara Alicja Jereczek-Fossa
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	systematic review artificial intelligence machine learning radiotherapy head and neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/38f4a301a277435aba628cbb701dbb26
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spelling	oai:doaj.org-article:38f4a301a277435aba628cbb701dbb262021-11-18T10:43:09ZMachine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist2234-943X10.3389/fonc.2021.772663https://doaj.org/article/38f4a301a277435aba628cbb701dbb262021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.772663/fullhttps://doaj.org/toc/2234-943XBackground and PurposeMachine 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).Materials and MethodsElectronic 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.ResultsForty-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).Discussion and ConclusionThe 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.Stefania VolpeStefania VolpeMatteo PepaMattia ZaffaroniFederica BellerbaRiccardo SantamariaRiccardo SantamariaGiulia MarvasoGiulia MarvasoLars Johannes IsakssonSara GandiniAnna StarzyńskaMaria Cristina LeonardiRoberto OrecchiaDaniela AlterioBarbara Alicja Jereczek-FossaBarbara Alicja Jereczek-FossaFrontiers Media S.A.articlesystematic reviewartificial intelligencemachine learningradiotherapyhead and neck cancerNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	systematic review artificial intelligence machine learning radiotherapy head and neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	systematic review artificial intelligence machine learning radiotherapy head and neck cancer Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Stefania Volpe Stefania Volpe Matteo Pepa Mattia Zaffaroni Federica Bellerba Riccardo Santamaria Riccardo Santamaria Giulia Marvaso Giulia Marvaso Lars Johannes Isaksson Sara Gandini Anna Starzyńska Maria Cristina Leonardi Roberto Orecchia Daniela Alterio Barbara Alicja Jereczek-Fossa Barbara Alicja Jereczek-Fossa Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
description	Background and PurposeMachine 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).Materials and MethodsElectronic 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.ResultsForty-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).Discussion and ConclusionThe 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.
format	article
author	Stefania Volpe Stefania Volpe Matteo Pepa Mattia Zaffaroni Federica Bellerba Riccardo Santamaria Riccardo Santamaria Giulia Marvaso Giulia Marvaso Lars Johannes Isaksson Sara Gandini Anna Starzyńska Maria Cristina Leonardi Roberto Orecchia Daniela Alterio Barbara Alicja Jereczek-Fossa Barbara Alicja Jereczek-Fossa
author_facet	Stefania Volpe Stefania Volpe Matteo Pepa Mattia Zaffaroni Federica Bellerba Riccardo Santamaria Riccardo Santamaria Giulia Marvaso Giulia Marvaso Lars Johannes Isaksson Sara Gandini Anna Starzyńska Maria Cristina Leonardi Roberto Orecchia Daniela Alterio Barbara Alicja Jereczek-Fossa Barbara Alicja Jereczek-Fossa
author_sort	Stefania Volpe
title	Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
title_short	Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
title_full	Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
title_fullStr	Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
title_full_unstemmed	Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist
title_sort	machine learning for head and neck cancer: a safe bet?—a clinically oriented systematic review for the radiation oncologist
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/38f4a301a277435aba628cbb701dbb26
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Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist

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