ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1587788
This article is part of the Research TopicAI-Based Prognosis Prediction and Dose Optimization Strategy in Radiotherapy for Malignant TumorsView all 11 articles
A New Deep Learning Model for Predicting IMRT Dose Distributions for Lung Cancer with Dose Masks
Provisionally accepted
- 1Department of Radiation Oncology, Peking University Third Hospital, Haidian, China
- 2School of Nuclear Science and Technology, University of South China, Hengyang, Hunan Province, China
- 3School of Physics, Beihang University, Beijing, Beijing Municipality, China
- 4Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing, Beijing Municipality, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 numbers of dose masks to investigate their impact on dose prediction models. Results:The inclusion of dose masks led to significant improvements in prediction accuracy for both the PTV and OARs. In particular, the mean absolute error (MAE) of dosimetric metrics for most OARs fell below 2%, and voxel-wise MAE within each structure steadily decreased as more dose masks were supplied-most notably in low-dose regions. These results demonstrate that incorporating dose masks effectively enhances training efficiency and prediction stability. Among models receiving varying numbers of dose masks, the configuration with ten masks achieved the highest predictive accuracy. Conclusion: This study proposes a dose mask-assisted method for lung cancer IMRT dose prediction. It demonstrates high accuracy and robustness in clinical radiotherapy scenarios with various prescription schemes, including conventional radiotherapy and SIB. The inclusion of additional dose masks significantly improved model performance, with prediction accuracy increasing as the number of masks increased.
Keywords: deep learning, IMRT, dose prediction, Radiotherapy treatment planning, lung cancer
Received: 04 Mar 2025; Accepted: 30 Jul 2025.
Copyright: © 2025 Feng, Wang, Lin, Li, Pan, Zuo and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Guoping Zuo, School of Nuclear Science and Technology, University of South China, Hengyang, 421001, Hunan Province, China
Ruijie Yang, Department of Radiation Oncology, Peking University Third Hospital, Haidian, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.