Cited by 302Advanced Radiotherapy TechniquesArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Survey on deep learning for radiotherapy

Meyer, Philippe, Noblet, Vincent, Mazzara, Christophe, Lallement, Alex·2018·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2018.05.018

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Abstract

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.

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Authors: Meyer, P., Noblet, V., Mazzara, C., & Lallement, A.
Affiliations: Institut Strauss (FR)
Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
Corresponding author: Meyer, Philippe
ORCID: Meyer, Philippe
Noblet, Vincent
Mazzara, Christophe
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2018-07
Volume: 98
Pages: 126-146
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2018.05.018
OpenAlex: W2804608955
Cited by: 302
References: 237
Citations by year: 2026: 11 · 2025: 13 · 2024: 38 · 2023: 38 · 2022: 46 · 2021: 60 · 2020: 48 · 2019: 37 · 2018: 11
FWCI: 5.52
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Computers in Biology and Medicine (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Topics: Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: deep learning, radiotherapy, radiotherapy treatment planning, convolutional neural networks, artificial intelligence in healthcare, machine learning, network architecture, patient workflow
MeSH terms: Deep Learning, Humans, Nasopharyngeal Neoplasms, Radiotherapy, Radiotherapy Planning, Computer-Assisted

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