Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

Ying Song; Junjie Hu; Yang Liu; Haiyun Hu; Yang Huang; Sen Bai; Zhang Yi

doi:10.1016/j.radonc.2020.05.005

Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

Radiother Oncol. 2020 Aug:149:111-116. doi: 10.1016/j.radonc.2020.05.005. Epub 2020 May 19.

Authors

Ying Song¹, Junjie Hu², Yang Liu³, Haiyun Hu², Yang Huang², Sen Bai⁴, Zhang Yi⁵

Affiliations

¹ Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, PR China; Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China.
² Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, PR China.
³ Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China; Department of Radiotherapy, The First People's Hospital of Yibin, PR China. Electronic address: liuyanghe1991@foxmail.com.
⁴ Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China. Electronic address: baisen@scu.edu.cn.
⁵ Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, PR China. Electronic address: zhangyi@scu.edu.cn.

PMID: 32416279
DOI: 10.1016/j.radonc.2020.05.005

Abstract

Purpose: To apply a deep neural network to predict dose distributions of rectal cancer patients for accelerated volume modulated arc technique (VMAT) planning.

Materials and methods: Computed tomography scans and approved VMAT plans together with Dose_approved of 187 patients treated from February 2018 to April 2019 were randomly selected for this retrospective study. The deep neural network DeepLabv3+ was applied for dose distribution prediction. A prior dose information-aided planning scheme was introduced. Prediction precision was evaluated by mean square error (MSE), normalized dose difference (δD), and dose-volume histogram (DVH) indices using a paired t test. Information-aided and experienced replanning were performed by 1-year and 6-year experienced dosimetrists, respectively. Replanning time and DVH indices were evaluated by two-way variance analysis.

Results: The DeepLabv3+ prediction results (Dose_DeepLabv3+) were all clinically acceptable. Taking Dose_approved as the baseline, the MSE was 0.001 and mean δD was 0.40% with an inter-quartile range of 0.079%-0.30% for Dose_DeepLabv3+. No significant differences were found for the planning target volume quantitative parameters between Dose_approved and Dose_DeepLabv3+, except for the conformality index. For the two-way variance analysis, a significantly different replanning time was found between the information-aided and experienced replanning with maximum time-saving of 15.76 min. Information-aided replans had the advantage of lower maximum dose, higher minimum dose, and lower homogeneity index, and the disadvantage of lower conformality index and higher machine unites with significant differences.

Conclusion: DeepLabv3+ successfully predicted rectal cancer dose distribution, and the predicted prior information helped save planning times for multi-level experienced dosimetrists.

Keywords: Deep neural network; Dose distribution; Radiotherapy planning; Rectal cancer.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Neural Networks, Computer
Organs at Risk
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Intensity-Modulated*
Rectal Neoplasms* / radiotherapy
Retrospective Studies