PMID- 31236943
OWN - NLM
STAT- MEDLINE
DCOM- 20200124
LR  - 20200901
IS  - 2473-4209 (Electronic)
IS  - 0094-2405 (Print)
IS  - 0094-2405 (Linking)
VI  - 46
IP  - 9
DP  - 2019 Sep
TI  - Modeling of multiple planning target volumes for head and neck treatments in 
      knowledge-based treatment planning.
PG  - 3812-3822
LID - 10.1002/mp.13679 [doi]
AB  - PURPOSE: The purpose of this study is to develop an accurate and reliable dose 
      volume histogram (DVH) prediction method for external beam radiation therapy 
      plans with multiple planning target volumes (PTVs). MATERIALS AND METHODS: We 
      present a novel DVH prediction workflow, including new features and a modeling 
      methodology, that makes better use of multiple PTVs: (a) We propose a generalized 
      feature to characterize the geometric relationship of organ-at-risk (OARs) with 
      respect to two or more PTVs with different prescribed dose levels; (b) We 
      incorporate a novel data augmentation method to improve the data distribution in 
      the feature space; (c) A similarity metric that leverages such information is 
      subsequently used to select a subset of similar cases from the training dataset 
      for model building; (d) Finally, a DVH prediction model is trained with these 
      selected cases. To evaluate this new modeling workflow, we used 120 head and neck 
      (HN) cases to tune the model, and used a separate dataset consisting of 148 cases 
      for validation. The proposed model has been compared with the conventional 
      knowledge-based model in terms of model prediction accuracy, which was measured 
      by the root mean squared error (RMSE) between the predicted DVHs and the actual 
      clinical plan DVHs. Furthermore, 25 randomly selected plans were replanned guided 
      by the proposed model and evaluated against clinical plans using clinical 
      evaluation criteria. RESULTS: The proposed modeling workflow significantly 
      improved DVH prediction accuracy for brainstem (P < 0.001), cord (P < 0.001), 
      larynx (P = 0.004), mandible (P < 0.001), oral cavity (P = 0.011), parotid 
      (P < 0.001) and pharynx (P = 0.001). Cases replanned with the guidance of the 
      proposed model spared OARs significantly better by clinical evaluation criteria. 
      The replanned cases showed a 15% increase in the number of satisfied criteria, 
      compared with clinical plans. CONCLUSIONS: The proposed modeling workflow 
      generates DVH predictions with improved accuracy and robustness when multiple 
      PTVs exist in a plan. It has demonstrated that the improvement in the DVH 
      prediction model translates into better plan quality in knowledge-based planning.
CI  - (c) 2019 American Association of Physicists in Medicine.
FAU - Zhang, Jiahan
AU  - Zhang J
AD  - Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 
      27710, USA.
FAU - Ge, Yaorong
AU  - Ge Y
AD  - Department of Software and Information Systems, University of North Carolina at 
      Charlotte, Charlotte, NC, 28223, USA.
FAU - Sheng, Yang
AU  - Sheng Y
AD  - Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 
      27710, USA.
FAU - Yin, Fang-Fang
AU  - Yin FF
AD  - Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 
      27710, USA.
FAU - Wu, Q Jackie
AU  - Wu QJ
AD  - Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 
      27710, USA.
LA  - eng
GR  - R01 CA201212/CA/NCI NIH HHS/United States
GR  - #R01CA201212/GF/NIH HHS/United States
PT  - Journal Article
DEP - 20190717
PL  - United States
TA  - Med Phys
JT  - Medical physics
JID - 0425746
SB  - IM
MH  - Head and Neck Neoplasms/*radiotherapy
MH  - Humans
MH  - Organs at Risk/radiation effects
MH  - Radiotherapy Dosage
MH  - Radiotherapy Planning, Computer-Assisted/*methods
MH  - *Radiotherapy, Intensity-Modulated/adverse effects
PMC - PMC6739188
MID - NIHMS1037536
OTO - NOTNLM
OT  - DVH prediction
OT  - Intensity-modulated radiation therapy
OT  - knowledge-based planning
OT  - modeling
OT  - treatment planning
COIS- The authors have no conflicts to disclose.
EDAT- 2019/06/27 06:00
MHDA- 2020/01/25 06:00
PMCR- 2020/09/01
CRDT- 2019/06/26 06:00
PHST- 2019/03/06 00:00 [received]
PHST- 2019/06/10 00:00 [revised]
PHST- 2019/06/13 00:00 [accepted]
PHST- 2019/06/27 06:00 [pubmed]
PHST- 2020/01/25 06:00 [medline]
PHST- 2019/06/26 06:00 [entrez]
PHST- 2020/09/01 00:00 [pmc-release]
AID - MP13679 [pii]
AID - 10.1002/mp.13679 [doi]
PST - ppublish
SO  - Med Phys. 2019 Sep;46(9):3812-3822. doi: 10.1002/mp.13679. Epub 2019 Jul 17.