Cargando…
Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers
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 clinic...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445892/ https://www.ncbi.nlm.nih.gov/pubmed/30972291 http://dx.doi.org/10.3389/fonc.2019.00174 |
_version_ | 1783408263964917760 |
---|---|
author | Giraud, Paul Giraud, Philippe Gasnier, Anne El Ayachy, Radouane Kreps, Sarah Foy, Jean-Philippe Durdux, Catherine Huguet, Florence Burgun, Anita Bibault, Jean-Emmanuel |
author_facet | Giraud, Paul Giraud, Philippe Gasnier, Anne El Ayachy, Radouane Kreps, Sarah Foy, Jean-Philippe Durdux, Catherine Huguet, Florence Burgun, Anita Bibault, Jean-Emmanuel |
author_sort | Giraud, Paul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6445892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64458922019-04-10 Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers Giraud, Paul Giraud, Philippe Gasnier, Anne El Ayachy, Radouane Kreps, Sarah Foy, Jean-Philippe Durdux, Catherine Huguet, Florence Burgun, Anita Bibault, Jean-Emmanuel Front Oncol Oncology 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. Frontiers Media S.A. 2019-03-27 /pmc/articles/PMC6445892/ /pubmed/30972291 http://dx.doi.org/10.3389/fonc.2019.00174 Text en Copyright © 2019 Giraud, Giraud, Gasnier, El Ayachy, Kreps, Foy, Durdux, Huguet, Burgun and Bibault. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Giraud, Paul Giraud, Philippe Gasnier, Anne El Ayachy, Radouane Kreps, Sarah Foy, Jean-Philippe Durdux, Catherine Huguet, Florence Burgun, Anita Bibault, Jean-Emmanuel Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title | Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title_full | Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title_fullStr | Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title_full_unstemmed | Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title_short | Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers |
title_sort | radiomics and machine learning for radiotherapy in head and neck cancers |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445892/ https://www.ncbi.nlm.nih.gov/pubmed/30972291 http://dx.doi.org/10.3389/fonc.2019.00174 |
work_keys_str_mv | AT giraudpaul radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT giraudphilippe radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT gasnieranne radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT elayachyradouane radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT krepssarah radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT foyjeanphilippe radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT durduxcatherine radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT huguetflorence radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT burgunanita radiomicsandmachinelearningforradiotherapyinheadandneckcancers AT bibaultjeanemmanuel radiomicsandmachinelearningforradiotherapyinheadandneckcancers |