A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Feng, Xuezhen;Wang, Mingqing;Lin, Xinyan;Li, Can;Pan, Yuxi;...

通讯作者：

Zuo, GP;Yang, RJ

作者机构：

[Feng, Xuezhen; Zuo, Guoping] Univ South China, Sch Nucl Sci & Technol, Hengyang, Peoples R China.

[Lin, Xinyan; Feng, Xuezhen; Wang, Mingqing; Yang, Ruijie; Pan, Yuxi; Li, Can] Peking Univ Third Hosp, Canc Ctr, Dept Radiat Oncol, Beijing, Peoples R China.

[Lin, Xinyan] Beihang Univ, Sch Phys, Beijing, Peoples R China.

[Li, Can] Beijing Univ Technol, Inst Operat Res & Informat Engn, Beijing, Peoples R China.

通讯机构：

[Zuo, GP ] U

[Yang, RJ ] P

Univ South China, Sch Nucl Sci & Technol, Hengyang, Peoples R China.

Peking Univ Third Hosp, Canc Ctr, Dept Radiat Oncol, Beijing, Peoples R 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：

页码：

1587788

DOI：

10.3389/fonc.2025.1587788

基金类别：

National Key Research and Development Program [2024YFA1014104]; Beijing Municipal Commission of Science and Technology Collaborative Innovation Project [Z221100003522028]

机构署名：

本校为第一且通讯机构

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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A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks

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