PMID- 38509540
OWN - NLM
STAT- MEDLINE
DCOM- 20240322
LR  - 20240323
IS  - 1748-717X (Electronic)
IS  - 1748-717X (Linking)
VI  - 19
IP  - 1
DP  - 2024 Mar 20
TI  - Automatic IMRT treatment planning through fluence prediction and plan fine-tuning 
      for nasopharyngeal carcinoma.
PG  - 39
LID - 10.1186/s13014-024-02401-0 [doi]
LID - 39
AB  - BACKGROUND: At present, the implementation of intensity-modulated radiation 
      therapy (IMRT) treatment planning for geometrically complex nasopharyngeal 
      carcinoma (NPC) through manual trial-and-error fashion presents challenges to the 
      improvement of planning efficiency and the obtaining of high-consistency plan 
      quality. This paper aims to propose an automatic IMRT plan generation method 
      through fluence prediction and further plan fine-tuning for patients with NPC and 
      evaluates the planning efficiency and plan quality. METHODS: A total of 38 
      patients with NPC treated with nine-beam IMRT were enrolled in this study and 
      automatically re-planned with the proposed method. A trained deep learning model 
      was employed to generate static field fluence maps for each patient with 3D 
      computed tomography images and structure contours as input. Automatic IMRT 
      treatment planning was achieved by using its generated dose with slight 
      tightening for further plan fine-tuning. Lastly, the plan quality was compared 
      between automatic plans and clinical plans. RESULTS: The average time for 
      automatic plan generation was less than 4 min, including fluence maps prediction 
      with a python script and automated plan tuning with a C# script. Compared with 
      clinical plans, automatic plans showed better conformity and homogeneity for 
      planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the 
      dosimetric metrics for most organs at risk (OARs) were ameliorated in the 
      automatic plan, especially D(max) of the brainstem and spinal cord, and D(mean) 
      of the left and right parotid glands significantly decreased (P < 0.05). 
      CONCLUSION: We have successfully implemented an automatic IMRT plan generation 
      method for patients with NPC. This method shows high planning efficiency and 
      comparable or superior plan quality than clinical plans. The qualitative results 
      before and after the plan fine-tuning indicates that further optimization using 
      dose objectives generated by predicted fluence maps is crucial to obtain 
      high-quality automatic plans.
CI  - (c) 2024. The Author(s).
FAU - Cai, Wenwen
AU  - Cai W
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, 
      China.
FAU - Ding, Shouliang
AU  - Ding S
AD  - Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key 
      Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer 
      Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and 
      Therapy, Guangzhou, 510060, China.
FAU - Li, Huali
AU  - Li H
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, 
      China.
FAU - Zhou, Xuanru
AU  - Zhou X
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, 
      China.
FAU - Dou, Wen
AU  - Dou W
AD  - Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
FAU - Zhou, Linghong
AU  - Zhou L
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, 
      China.
FAU - Song, Ting
AU  - Song T
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, 
      China. tingsong2015@smu.edu.cn.
FAU - Li, Yongbao
AU  - Li Y
AD  - Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key 
      Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer 
      Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and 
      Therapy, Guangzhou, 510060, China. liyb1@sysucc.org.cn.
LA  - eng
GR  - 2021A1515012044/Guangdong Basic and Applied Basic Research Foundation, China/
GR  - 2022A1515010639/Guangdong Basic and Applied Basic Research Foundation, China/
GR  - SL2022A04J01215/Guangzhou Science and Technology Foundation, China/
PT  - Journal Article
DEP - 20240320
PL  - England
TA  - Radiat Oncol
JT  - Radiation oncology (London, England)
JID - 101265111
SB  - IM
MH  - Humans
MH  - Nasopharyngeal Carcinoma/radiotherapy
MH  - *Radiotherapy, Intensity-Modulated/methods
MH  - Radiotherapy Dosage
MH  - Radiotherapy Planning, Computer-Assisted/methods
MH  - Organs at Risk
MH  - *Nasopharyngeal Neoplasms/radiotherapy
PMC - PMC10956235
OTO - NOTNLM
OT  - Automatic planning
OT  - Fluence prediction
OT  - IMRT
OT  - Nasopharyngeal carcinoma
OT  - Plan fine-tuning
COIS- The authors declare that they have no competing interests.
EDAT- 2024/03/21 06:43
MHDA- 2024/03/22 06:44
PMCR- 2024/03/20
CRDT- 2024/03/21 00:48
PHST- 2023/08/14 00:00 [received]
PHST- 2024/01/09 00:00 [accepted]
PHST- 2024/03/22 06:44 [medline]
PHST- 2024/03/21 06:43 [pubmed]
PHST- 2024/03/21 00:48 [entrez]
PHST- 2024/03/20 00:00 [pmc-release]
AID - 10.1186/s13014-024-02401-0 [pii]
AID - 2401 [pii]
AID - 10.1186/s13014-024-02401-0 [doi]
PST - epublish
SO  - Radiat Oncol. 2024 Mar 20;19(1):39. doi: 10.1186/s13014-024-02401-0.