Survey on deep learning for radiotherapy
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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Convolutional neural networks can reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude without significant reduction in image quality.
“They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [88] without significant reduction in image quality.”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [88]
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Sub‐second photon dose prediction via transformer neural networks
Pastor‐Serrano, O., Dong, P., Huang, C. W., Xing, L., & Perkó, Z. (2023) · Medical Physics · doi:10.1002/mp.16231 · Cited by 27 · see citing works
- 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
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 98
- Pages
- 126-146
- ISSN
- 0010-4825 (ISSN-L)
- 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
- 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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