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Automatic treatment planning based on three‐dimensional dose distribution predicted from deep learning technique
Author(s) -
Fan Jiawei,
Wang Jiazhou,
Chen Zhi,
Hu Chaosu,
Zhang Zhen,
Hu Weigang
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13271
Subject(s) - radiation treatment planning , deep learning , artificial intelligence , nuclear medicine , pinnacle , residual , histogram , dosimetry , medicine , radiation therapy , computer science , pattern recognition (psychology) , machine learning , algorithm , radiology , image (mathematics)
Purpose To develop an automated treatment planning strategy for external beam intensity‐modulated radiation therapy ( IMRT ), including a deep learning‐based three‐dimensional (3D) dose prediction and a dose distribution‐based plan generation algorithm. Methods and Materials A residual neural network‐based deep learning model is trained to predict a dose distribution based on patient‐specific geometry and prescription dose. A total of 270 head‐and‐neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk ( OAR s) and planning target volumes ( PTV s). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation. Results Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose–volume histogram ( DVH ) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV 70.4 , but the difference is only 0.5% which is still clinically acceptable. Conclusion This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution‐based optimization. It is a promising approach for realizing automated treatment planning in the future.