PMID- 30383300
OWN - NLM
STAT- MEDLINE
DCOM- 20190121
LR  - 20190121
IS  - 2473-4209 (Electronic)
IS  - 0094-2405 (Linking)
VI  - 46
IP  - 1
DP  - 2019 Jan
TI  - Automatic treatment planning based on three-dimensional dose distribution 
      predicted from deep learning technique.
PG  - 370-381
LID - 10.1002/mp.13271 [doi]
AB  - 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 (OARs) and planning target volumes (PTVs). 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.
CI  - (c) 2018 American Association of Physicists in Medicine.
FAU - Fan, Jiawei
AU  - Fan J
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
FAU - Wang, Jiazhou
AU  - Wang J
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
FAU - Chen, Zhi
AU  - Chen Z
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
AD  - Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai, 
      201321, China.
FAU - Hu, Chaosu
AU  - Hu C
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
FAU - Zhang, Zhen
AU  - Zhang Z
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
FAU - Hu, Weigang
AU  - Hu W
AD  - Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 
      Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 
      200032, China.
LA  - eng
GR  - 11675042/National Natural Science Foundation of China/
GR  - 11805039/National Natural Science Foundation of China/
GR  - SKQY1601/Shanghai Emerging Frontier Technology Joint Research/
PT  - Journal Article
DEP - 20181128
PL  - United States
TA  - Med Phys
JT  - Medical physics
JID - 0425746
SB  - IM
MH  - Algorithms
MH  - Automation
MH  - *Deep Learning
MH  - Head and Neck Neoplasms/radiotherapy
MH  - Humans
MH  - *Radiation Dosage
MH  - Radiotherapy Dosage
MH  - Radiotherapy Planning, Computer-Assisted/*methods
MH  - Radiotherapy, Intensity-Modulated
OTO - NOTNLM
OT  - deep learning
OT  - dose distribution prediction
OT  - knowledge-based planning
OT  - voxel-by-voxel dose optimization
EDAT- 2018/11/02 06:00
MHDA- 2019/01/22 06:00
CRDT- 2018/11/02 06:00
PHST- 2018/07/19 00:00 [received]
PHST- 2018/10/16 00:00 [revised]
PHST- 2018/10/26 00:00 [accepted]
PHST- 2018/11/02 06:00 [pubmed]
PHST- 2019/01/22 06:00 [medline]
PHST- 2018/11/02 06:00 [entrez]
AID - 10.1002/mp.13271 [doi]
PST - ppublish
SO  - Med Phys. 2019 Jan;46(1):370-381. doi: 10.1002/mp.13271. Epub 2018 Nov 28.