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人工智能在放射治疗剂量预测中的应用与进展

Application and progress of artificial intelligence in radiation therapy dose prediction.

作者信息

Jiang Chen, Ji Tianlong, Qiao Qiao

机构信息

Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China.

出版信息

Clin Transl Radiat Oncol. 2024 May 9;47:100792. doi: 10.1016/j.ctro.2024.100792. eCollection 2024 Jul.

Abstract

Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a time-consuming and labor-intensive process. In addition, due to the differences in knowledge and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) has been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority. In this paper, we will review the research of AI in dose prediction, focusing on the progress in deep learning (DL).

摘要

如今,放射治疗(RT)是癌症的主要治疗方式。为确保患者的治疗效果,通常需要精确的剂量分布,而这是一个耗时且费力的过程。此外,由于参与者和不同机构之间知识和经验的差异,预测剂量往往不一致。在过去几十年中,人工智能(AI)已应用于放射治疗的各个方面,一些产品已在临床实践中得到应用并证实了其优越性。在本文中,我们将回顾人工智能在剂量预测方面的研究,重点关注深度学习(DL)的进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b88/11109740/3d0c65c3724c/gr1.jpg

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