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. 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. 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. 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/.

翻译：视觉Transformer方法正在推动医学人工智能和癌症影像领域的发展，包括肺癌应用。近年来，许多研究者开发了基于视觉Transformer的AI方法用于肺癌诊断与预后。本范围综述旨在识别近期基于视觉Transformer的AI方法在肺癌影像应用中的进展，揭示了视觉Transformer如何提升AI和深度学习方法在肺癌领域的性能表现。此外，综述还识别了推动该领域发展的数据集。在检索到的314篇研究中，本综述纳入了2020至2022年间发表的34项研究。这些研究中最常见的任务是肺癌类型分类（如肺鳞状细胞癌与肺腺癌的区分）以及良恶性肺结节的识别。其他应用包括肺癌患者生存预测和肺部分割。现有研究缺乏明确的临床转化策略。SWIN Transformer是研究者广泛采用的架构，但其他多种架构亦有报道，其中视觉Transformer常与卷积神经网络或UNet模型结合。可以得出结论：基于视觉Transformer的模型在开发肺癌AI方法中日渐普及。然而，其计算复杂性和临床相关性是未来研究需考虑的重要因素。本综述为人工智能与医疗领域的研究者提供了宝贵见解，有助于推动肺癌诊断与预后技术的先进水平发展。我们提供了交互式仪表盘，可访问 lung-cancer.onrender.com/。