Deep learning applications in motion management for radiotherapy

Bining Yang; Ran Wei; Jianrong Dai

doi:10.1016/j.intonc.2025.06.006

您当前的位置：

首页 >

文章列表页 >

Deep learning applications in motion management for radiotherapy

Review article | 更新时间：2026-06-03

- Deep learning applications in motion management for radiotherapy
- Deep learning applications in motion management for radiotherapy
- 智能肿瘤学 2025年1卷第3期页码：244-255
- 作者机构：
  
  National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing,China,100021
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2025.06.006
  中图分类号：
- 收稿：2025-04-17，
  
  网络首发：2025-07-01，
  
  纸质出版：2025-07-25
- 稿件说明：
移动端阅览
Bining Yang, Ran Wei, Jianrong Dai. Deep learning applications in motion management for radiotherapy[J]. 智能肿瘤学, 2025,1(3):244-255.

Bining Yang, Ran Wei, Jianrong Dai. Deep learning applications in motion management for radiotherapy[J]. Intelligent Oncology, 2025, 1(3): 244-255.
Bining Yang, Ran Wei, Jianrong Dai. Deep learning applications in motion management for radiotherapy[J]. 智能肿瘤学, 2025,1(3):244-255. DOI： 10.1016/j.intonc.2025.06.006.

Bining Yang, Ran Wei, Jianrong Dai. Deep learning applications in motion management for radiotherapy[J]. Intelligent Oncology, 2025, 1(3): 244-255. DOI： 10.1016/j.intonc.2025.06.006.

摘要

The aim of radiotherapy (RT) is to deliver prescribed doses to tumors while sparing neighboring organs at risk. As the demand for treatment precision increases in modern RT

intrafractional motion management becomes critical for achieving high precision

particularly for tumors in the thorax and abdomen that are affected by respiration and other physiological motions. Deep learning (DL) has demonstrated strong potential in addressing the limitations of conventional methods by enabling rapid and accurate tumor motion detection

tumor location prediction

and management measures. This review provides a comprehensive overview of recent DL-based applications in intrafractional motion management

which are categorized into three areas: (1) tumor motion detection for real-time tumor localization

(2) tumor location prediction to compensate for the latency of the motion management system

and (3) management measures

including gating and real-time adaptive RT. In addition

this review discusses key challenges and their potential solutions for DL-based motion management.

Abstract

The aim of radiotherapy (RT) is to deliver prescribed doses to tumors while sparing neighboring organs at risk. As the demand for treatment precision increases in modern RT

intrafractional motion management becomes critical for achieving high precision

tumor location prediction

and management measures. This review provides a comprehensive overview of recent DL-based applications in intrafractional motion management

which are categorized into three areas: (1) tumor motion detection for real-time tumor localization

(2) tumor location prediction to compensate for the latency of the motion management system

and (3) management measures

including gating and real-time adaptive RT. In addition

this review discusses key challenges and their potential solutions for DL-based motion management.

关键词

Keywords

references

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

From morphology to function: Advances in multimodal imaging for pulmonary function prediction

AI-based diagnosis of clear-cell renal cell carcinoma based on non-contrast CT

Deep learning-based segmentation of small-volume brain metastases in lung cancer patients

Multimodal medical imaging AI for breast cancer diagnosis: A comprehensive review

Deep learning-based localization of preoperative parathyroid glands in secondary hyperparathyroidism patients using dual-phase unenhanced and contrast-enhanced computed tomography data