Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36835
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review
Author(s): Ali, Hazrat
Mohsen, Farida
Shah, Zubair
Contact Email: ali.hazrat@stir.ac.uk
Keywords: Adenocarcinoma
Artificial intelligence
Convolutional neural networks
Deep learning
Diagnosis
Lung Cancer
Medical imaging
Segmentation
Survival prediction
Vision Transformers
Issue Date: 15-Sep-2023
Date Deposited: 27-Jan-2025
Citation: Ali H, Mohsen F & Shah Z (2023) Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review. <i>BMC Medical Imaging</i>, 23, Art. No.: 129. https://doi.org/10.1186/s12880-023-01098-z
Abstract: Background Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. Objective This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. Methods In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. Results Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. Conclusion It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/.
DOI Link: 10.1186/s12880-023-01098-z
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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