scholarly journals Improving efficiency of training a virtual treatment planner network via knowledge‐guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy

2021 ◽  
Author(s):  
Chenyang Shen ◽  
Liyuan Chen ◽  
Yesenia Gonzalez ◽  
Xun Jia
Keyword(s):  
Reinforcement Learning ◽  
Treatment Planning ◽  
Automatic Treatment ◽  
Virtual Treatment

scholarly journals Virtual treatment planning solution

BDJ ◽  
2010 ◽  
Vol 208 (1) ◽  
pp. 36-36
Keyword(s):  
Treatment Planning ◽  
Virtual Treatment Planning ◽  
Virtual Treatment

scholarly journals Automatic treatment planning for VMAT‐based total body irradiation using Eclipse scripting

2021 ◽  
Vol 22 (3) ◽  
pp. 119-130
Author(s):  
Jose R. Teruel ◽  
Sameer Taneja ◽  
Paulina E. Galavis ◽  
K. Sunshine Osterman ◽  
Allison McCarthy ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Total Body Irradiation ◽  
Total Body ◽  
Automatic Treatment

Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique

2018 ◽  
Vol 46 (1) ◽  
pp. 370-381 ◽  
Author(s):  
Jiawei Fan ◽  
Jiazhou Wang ◽  
Zhi Chen ◽  
Chaosu Hu ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Dose Distribution ◽  
Three Dimensional ◽  
Learning Technique ◽  
Automatic Treatment

Deep reinforcement learning for treatment planning in high-dose-rate cervical brachytherapy

2022 ◽  
Vol 94 ◽  
pp. 1-7
Author(s):  
Gang Pu ◽  
Shan Jiang ◽  
Zhiyong Yang ◽  
Yuanjing Hu ◽  
Ziqi Liu
Keyword(s):  
Reinforcement Learning ◽  
Dose Rate ◽  
Treatment Planning ◽  
High Dose Rate ◽  
High Dose

Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning

2021 ◽  
pp. 20210214
Author(s):  
Hanlin Wang ◽  
Ruoxi Wang ◽  
Jiacheng Liu ◽  
Jian Zhang ◽  
Kaining Yao ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Spinal Cord ◽  
Femoral Head ◽  
Treatment Planning ◽  
Planning Process ◽  
Conformity Index ◽  
Plan Quality ◽  
The Mean ◽  
Automatic Treatment ◽  
Better Than

Objectives: To develop and evaluate a practical automatic treatment planning method for Intensity-Modulated Radiation Therapy (IMRT) in cervical cancer cases. Methods: A novel algorithm named as Optimization Objectives Tree Search Algorithm (OOTSA) was proposed to emulate the planning optimization process and achieve a progressively improving IMRT plan, based on the Eclipse Scripting Application Programming Interface (ESAPI). Thirty previously treated cervical cancer cases were selected from the clinical database and comparison was made between the OOTSA-generated plans and clinical treated plans and RapidPlan-based (RP) plans. Results: In clinical evaluation, compared with plan scores of the clinical plans and the RP plans, 22 and 26 of the OOTSA plans were considered as clinically improved in terms of plan quality, respectively. The average conformity index (CI) for the PTV in the OOTSA plans was 0.86 ± 0.01 (mean ± 1 standard deviation), better than those in the RP plans (0.83 ± 0.02) and the clinical plans (0.71 ± 0.11). Compared with the clinical plans, the mean doses of femoral head, rectum, spinal cord and right kidney in the OOTSA plans were reduced by 2.34 ± 2.87 Gy, 1.67 ± 2.10 Gy, 4.12 ± 6.44 Gy and 1.15 ± 2.67 Gy. Compared with the RP plans, the mean doses of femoral head, spinal cord, right kidney and small intestine in the OOTSA plans were reduced by 3.31 ± 1.55 Gy, 4.25 ± 3.69 Gy, 1.54 ± 2.23 Gy and 3.33 ± 1.91 Gy, respectively. In the OOTSA plans, the mean dose of bladder was slightly increased, with 2.33 ± 2.55 Gy (versus clinical plans) and 1.37 ± 1.74 Gy (versus RP plans). The average elapsed time of OOTSA and clinical planning were 59.2 ± 3.47 min and 76.53 ± 5.19 min. Conclusions: The plans created by OOTSA have been shown marginally better than the manual plans, especially in preserving OARs. In addition, the time of automatic treatment planning has shown a reduction compared to a manual planning process, and the variation of plan quality was greatly reduced. Although improvement on the algorithm is warranted, this proof-of-concept study has demonstrated that the proposed approach can be a practical solution for automatic planning. Advances in knowledge: The proposed method is novel in the emulation strategy of the physicists’ iterative operation during the planning process. Based on the existing optimizers, this method can be a simple yet effective solution for automated IMRT treatment planning.

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