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A New Deep Learning Model for Predicting IMRT Dose Distributions for Lung Cancer with Dose Masks

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成果类型:
期刊论文
作者:
Feng, Xuezhen;Wang, Mingqing;Lin, Xinyan;Li, Can;Pan, Yuxi;...
作者机构:
[Feng, Xuezhen; Wang, Mingqing; Pan, Yuxi; Yang, Ruijie] Department of Radiation Oncology, Peking University Third Hospital, Haidian, China
[Lin, Xinyan] School of Physics, Beihang University, Beijing, Beijing Municipality, China
[Li, Can] Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing, Beijing Municipality, China
[Zuo, Guoping] School of Nuclear Science and Technology, University of South China, Hengyang, Hunan Province, China
语种:
英文
关键词:
IMRT;deep learning;dose prediction;lung cancer;radiotherapy treatment planning
期刊:
FRONTIERS IN ONCOLOGY
ISSN:
2234-943X
年:
2025
卷:
15
页码:
1587788
机构署名:
本校为第一机构
院系归属:
核科学技术学院
摘要:
PURPOSE: 3D U-Net deep neural networks are widely used for predicting radiotherapy dose distributions. However, dose prediction for lung cancer IMRT is limited to conventional radiotherapy, with significant errors in predicting the intermediate and low-dose regions. METHODS: We included a mixed dataset of conventional radiotherapy and simultaneous integrated boost (SIB) radiotherapy with various prescription schemes. In addition to inputting CT images and anatomical structures, we incorporated dose mask information to provide richer local low-dose details. We trained five models with varying n...

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