AUTHOR=Giraud Paul , Giraud Philippe , Gasnier Anne , El Ayachy Radouane , Kreps Sarah , Foy Jean-Philippe , Durdux Catherine , Huguet Florence , Burgun Anita , Bibault Jean-Emmanuel
TITLE=Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers
JOURNAL=Frontiers in Oncology
VOLUME=9
YEAR=2019
URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00174
DOI=10.3389/fonc.2019.00174
ISSN=2234-943X
ABSTRACT=Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow.
Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers.
Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation.
Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.