School of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Curr Med Imaging. 2023;19(11):1231-1244. doi: 10.2174/1573405619666230123104243.
Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT.
We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs.
In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8.
The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.
肺癌是癌症中死亡率最高的一种。放射治疗（RT）是治疗肺癌最有效的方法之一。正确地对肺肿瘤（LTs）和危及器官（OARs）进行分割是 RT 成功的基石。
我们在过去 10 年中在四个数据库中搜索了相关的材料：Web of Science、PubMed、Science Direct 和 Google Scholar。从 LT 和 OAR 的角度检查了基于深度学习的肺癌放射治疗分割技术（DSLC）研究的进展。
在本文中，调查文献中 LT 分割的大多数骰子相似系数（DSC）值都高于 0.7，而 OAR 分割的 DSC 指标均高于 0.8。
本综述的贡献在于总结了 DSLC 研究方法，并讨论了 DSLC 面临的问题以及可能的可行解决方案。本综述的目的是鼓励肺癌放射治疗和 DL 领域的专家之间的合作，并促进更多关于在肺癌放射治疗中使用 DL 的研究。
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