Study of automatic treatment planning of intensity-modulated radiotherapy based on deep learning technique for breast cancer patients
10.3760/cma.j.cn113030-20190320-00103
- VernacularTitle:基于深度学习方法的乳腺癌调强放疗自动计划研究
- Author:
Jiawei FAN
1
;
Zhi CHEN
;
Jiazhou WANG
;
Weigang HU
Author Information
1. 复旦大学附属肿瘤医院放射治疗中心 复旦大学上海医学院肿瘤学系 200032
- From:
Chinese Journal of Radiation Oncology
2020;29(8):671-675
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a deep learning-based approach for predicting the dose distribution of intensity-modulated radiotherapy (IMRT) for breast cancer patients, and evaluate the feasibility of applying the predicted dose distribution in the automatic treatment planning.Methods:A total of 240 patients with left breast cancer admitted to Fudan University Shanghai Cancer Center were enrolled in this study: 200 cases in the training dataset, 20 cases in the validation dataset and 20 cases in the testing dataset. A modified deep residual neural network was trained to establish the relationship between CT image, the contouring images of target area and organs at risk (OARs) and the dose distribution, aiming to predict the dose distribution. The predicted dose distribution was utilized as the optimization objective function to optimize and generate a high-quality plan.Results:Compared with the dose distribution of clinical treatment plan, the predicted dose distribution for target areas and OARs showed no statistical significance except for a simultaneous boost target PTV 48Gy. And the treatment plan generated based on the predicted dose distribution was basically consistent with the predicted outcomes. Conclusion:Our results demonstrate that the deep learning-based approach for predicting the dose distribution of IMRT for breast cancer contributes to further achieving the goal of automatic treatment planning.
