The study of dose prediction and automated plan for IMRT of postoperative esophageal cancer
10.19405/j.cnki.issn1000-1492.2023.02.019
- Author:
Wencheng Wang
1
;
Jieping Zhou
2
;
Peng Zhang
2
;
Ailin Wu
2
;
Aidong Wu
3
Author Information
1. School of Biomedical Engineering，Anhui Medical University，Hefei 230032
2. Dept of Radiation Oncology， The First Affiliated Hospital of University of Science and Technology of China，Hefei 230001
3. School of Biomedical Engineering，Anhui Medical University，Hefei 230032; Dept of Radiation Oncology， The First Affiliated Hospital of University of Science and Technology of China，Hefei 230001
- Publication Type:Journal Article
- Keywords:
esophageal cancer;
automated plan;
deep learning;
dosimetry;
intensity modulate radiotherapy
- From:
Acta Universitatis Medicinalis Anhui
2023;58(2):280-285
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the clinical dosimetry advantages of automated plan of IMRT for postoperative esophageal cancer and the dose prediction accuracy of the constructed 3D U-Res-Net model．
Methods:A total of 110 postoperative esophageal cancer (middle and upper) cases treated by IMRT were considered in the study，of which 90 cases were randomly selected for training of deep learning prediction model．The deep learning prediction model and Auto-Plan module ( Philips pinnacle3 16. 2 ) were used to predict the three-dimension dose distribution and redesigned the remaining 20 cases respectively ，and the results obtained were compared with manual plan.
Results :The average DSC value between the deep learning prediction plan and the manual plan was greater than 0. 92 in isodose surface，and the average Hausdorff distance HD95 of the isodose surface was 0. 58－0. 62 cm ; The V20 ，V30 ，Dmean of total lung were slightly lower than those of manual plan (P ＜0. 05 ) for the prediction model， meanwhile，the D2 ，D50 ，Dmean，HI of the target area and V30 of total lungs were better than those of manual plan(P ＜0. 05) for Auto-Plan ; Three-dimensional dose distribution of the three groups and the corresponding DVH curve showed that the three-dimensional dose distribution of the three groups had a little differences，and the DVH curves of the target area and organs at risk had a good agreement．
Conclusion: Auto-Plan can realize the design of automated plan for postoperative esophageal cancer，while the deep learning prediction model can realize the accurate prediction of the 3D dose distribution.
- Full text:2024071422245519414食管癌术后调强放疗剂量预测及自动计划研究_王文成.pdf
