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Feasibility Study of the Fluence-to-Dose Network (FDNet) for Patient-Specific IMRT Quality Assurance

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Abstract

The aim of this study is to predict the delivered dose distribution [Ddelivered(x, y)] with the use of a fluence-to-dose network (FDNet) to conduct patient-specific intensity-modulated radiation therapy (IMRT) quality assurance (pQA). The architecture of the FDNet was based on a convolutional neural network. Forty-four IMRT clinical cases of planned dose distributions for pQA [Dplanned(x, y)] and dynamic multileaf collimator (MLC) log files (Dynalog files) were collected. Using the Dynalog files, the expected fluence stack [Fexpected(x, y, t)] and the actual fluence stack [Factual(x, y, t)] were created from the expected and the actual machine parameters, respectively. The actual fluence stack, which was reconstructed from the partial information of the Dynalog file, corresponded to the control points of the Digital Imaging and Communications in Medicine radiation treatment plan and was denoted as [Factual(x, y, tpartial)]. The entire dataset was split into 11 subsets for the k-fold averaging cross-validation (k = 11). Ten (out of the 11) folds were used to train 10 candidate optimal FDNet models, and an ultimate FDNet was determined by averaging the parameters of the optimal models. The pQA was performed using the test data of the remaining fold with the ultimate FDNet. The dose distributions predicted using Factual(x, y, t) [Dpredicted(Factual(x, y, t))] and Factual(x, y, tpartial) [Dpredicted(Factual(x, y, tpartial))] were acquired. To evaluate the predicted pQA results, we conducted dosimetry using EBT3 films and an ion-chamber array detector (MatriXX). These dose distributions were compared with the Dplanned(x, y) by using a gamma analysis. The average gamma passing rates were determined based on the 3%/3 mm gamma criterion and were, respectively, equal to 98.49%, 97.21%, 97.23%, and 98.03%, for the Dpredicted(Factual(x, y, t)), Dpredicted(Factual(x, y, tpartial)), EBT3 film, and MatriXX. According to this study, the feasibility of the dose prediction method using the FDNet with complete Dynalog information was verified for the pQA. The respective differences of the average gamma passing rates for the Dpredicted(Factual(x, y, t)), and Dpredicted(Factual(x, y, tpartial)) were equal, respectively, to 1.28% and 2.88% according to the 3%/3 mm and the 2%/2 mm gamma criteria.

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Acknowledgments

This work was supported by the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (MSIP) (2017R1C1B2011257 and 2013M2A2A7043507).

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Authors and Affiliations

  1. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea

    Wonjoong Cheon

  2. Department of Radiation Oncology, Samsung Medical Center, Seoul, 06351, Korea

    Sung Jin Kim

  3. Department of Radiation Oncology, Chungnam National University Hospital, Daejeon, 35015, Korea

    Ui-Jung Hwang

  4. Department of Radiation Oncology, Chungbuk National University Hospital, Cheongju, 28644, Korea

    Byung Jun Min

  5. Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea

    Youngyih Han

  6. Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea

    Youngyih Han

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  1. Wonjoong Cheon
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  2. Sung Jin Kim
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  3. Ui-Jung Hwang
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Correspondence to Byung Jun Min or Youngyih Han.

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Cheon, W., Kim, S.J., Hwang, UJ. et al. Feasibility Study of the Fluence-to-Dose Network (FDNet) for Patient-Specific IMRT Quality Assurance. J. Korean Phys. Soc. 75, 724–734 (2019). https://doi.org/10.3938/jkps.75.724

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  • DOI: https://doi.org/10.3938/jkps.75.724

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