Deep Learning: A Review for the Radiation Oncologist
Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2019.00977/full |
| _version_ | 1811267236089823232 |
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| author | Luca Boldrini Jean-Emmanuel Bibault Carlotta Masciocchi Yanting Shen Martin-Immanuel Bittner |
| author_facet | Luca Boldrini Jean-Emmanuel Bibault Carlotta Masciocchi Yanting Shen Martin-Immanuel Bittner |
| author_sort | Luca Boldrini |
| collection | DOAJ |
| description | Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Methods: A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms “radiotherapy” and “deep learning.” In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Results: Studies using DL for image segmentation were identified in Brain (n = 2), Head and Neck (n = 3), Lung (n = 6), Abdominal (n = 2), and Pelvic (n = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling (n = 3), treatment response and survival (n = 2), or treatment planning (n = 5).Conclusion: Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice. |
| first_indexed | 2024-04-12T20:58:49Z |
| format | Article |
| id | doaj.art-186b2a9118934aa88e4b2d64e1a2dc99 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T20:58:49Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-186b2a9118934aa88e4b2d64e1a2dc992022-12-22T03:16:54ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-10-01910.3389/fonc.2019.00977436057Deep Learning: A Review for the Radiation OncologistLuca Boldrini0Jean-Emmanuel Bibault1Carlotta Masciocchi2Yanting Shen3Martin-Immanuel Bittner4Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, ItalyRadiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique—Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, FranceDipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, ItalyDepartment of Engineering Science, University of Oxford, Oxford, United KingdomCRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United KingdomIntroduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Methods: A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms “radiotherapy” and “deep learning.” In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later.Results: Studies using DL for image segmentation were identified in Brain (n = 2), Head and Neck (n = 3), Lung (n = 6), Abdominal (n = 2), and Pelvic (n = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling (n = 3), treatment response and survival (n = 2), or treatment planning (n = 5).Conclusion: Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice.https://www.frontiersin.org/article/10.3389/fonc.2019.00977/fullmachine learningdeep learningmodelingradiation oncologyclinical oncology |
| spellingShingle | Luca Boldrini Jean-Emmanuel Bibault Carlotta Masciocchi Yanting Shen Martin-Immanuel Bittner Deep Learning: A Review for the Radiation Oncologist Frontiers in Oncology machine learning deep learning modeling radiation oncology clinical oncology |
| title | Deep Learning: A Review for the Radiation Oncologist |
| title_full | Deep Learning: A Review for the Radiation Oncologist |
| title_fullStr | Deep Learning: A Review for the Radiation Oncologist |
| title_full_unstemmed | Deep Learning: A Review for the Radiation Oncologist |
| title_short | Deep Learning: A Review for the Radiation Oncologist |
| title_sort | deep learning a review for the radiation oncologist |
| topic | machine learning deep learning modeling radiation oncology clinical oncology |
| url | https://www.frontiersin.org/article/10.3389/fonc.2019.00977/full |
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