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Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

Publication ,  Journal Article
Yue, M; Xue, X; Wang, Z; Lambo, RL; Zhao, W; Xie, Y; Cai, J; Qin, W
Published in: Radiother Oncol
May 2022

BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking advantage of the distance information as well as the mask information. MATERIALS AND METHODS: A novel transformation method based on boundary distance was proposed to facilitate the prediction of dose distributions. Radiotherapy datasets of 161 nasopharyngeal carcinoma patients were retrospectively collected, including binary masks of organs-at-risk (OARs) and targets, planning CT, and clinical plans. The patients were randomly divided into 130, 11 and 20 cases for training, validating, and testing the models, respectively. Furthermore, 40 patients from an external cohort were used to test the generalizability of the models. RESULTS: The proposed method shows superior performance. The predicted dose error and dose-volume histogram (DVH) error of our method were 7.51% and 11.6% lower than the mask-based method, respectively. For the inverse planning, compared with mask-based methods, our method provided similar performances on the GTVnx and OARs and outperformed on the GTVnd and the CTV, the pass rates of which increased from 89.490% and 90.016% to 96.694% and 91.189%, respectively. CONCLUSION: The preliminary results on nasopharyngeal carcinoma radiotherapy cases showed that our proposed distance-guided method for dose prediction achieved better performance than mask-based methods. Further studies with more patients and on other cancer sites are warranted to fully validate the proposed method.

Duke Scholars

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Published In

Radiother Oncol

DOI

EISSN

1879-0887

Publication Date

May 2022

Volume

170

Start / End Page

198 / 204

Location

Ireland

Related Subject Headings

  • Retrospective Studies
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Nasopharyngeal Neoplasms
  • Nasopharyngeal Carcinoma
  • Humans
  • Deep Learning
 

Citation

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Chicago
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MLA
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Yue, M., Xue, X., Wang, Z., Lambo, R. L., Zhao, W., Xie, Y., … Qin, W. (2022). Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy. Radiother Oncol, 170, 198–204. https://doi.org/10.1016/j.radonc.2022.03.012
Yue, Meiyan, Xiaoguang Xue, Zhanyu Wang, Ricardo Lewis Lambo, Wei Zhao, Yaoqin Xie, Jing Cai, and Wenjian Qin. “Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.Radiother Oncol 170 (May 2022): 198–204. https://doi.org/10.1016/j.radonc.2022.03.012.
Yue M, Xue X, Wang Z, Lambo RL, Zhao W, Xie Y, et al. Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy. Radiother Oncol. 2022 May;170:198–204.
Yue, Meiyan, et al. “Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.Radiother Oncol, vol. 170, May 2022, pp. 198–204. Pubmed, doi:10.1016/j.radonc.2022.03.012.
Yue M, Xue X, Wang Z, Lambo RL, Zhao W, Xie Y, Cai J, Qin W. Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy. Radiother Oncol. 2022 May;170:198–204.
Journal cover image

Published In

Radiother Oncol

DOI

EISSN

1879-0887

Publication Date

May 2022

Volume

170

Start / End Page

198 / 204

Location

Ireland

Related Subject Headings

  • Retrospective Studies
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Nasopharyngeal Neoplasms
  • Nasopharyngeal Carcinoma
  • Humans
  • Deep Learning