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Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction

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Abstract

Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as “IVPSQA.” The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

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Availability of Data and Materials

The datasets during the current study are not publicly available due to some research that has not been completed, but is available from the corresponding author on reasonable request.

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Funding

The Science and Technology Project of Jiangxi Provincial Health Commission of China (No. 202310023).

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Author notes

  1. Zhongsheng Zou and Changfei Gong contributed equally.

Authors and Affiliations

  1. Department of Electronic Information Engineering, Nanchang University, Nanchang, China

    Zhongsheng Zou, Lingpeng Zeng, Yu Guan, Bin Huang, Xiuwen Yu, Qiegen Liu & Minghui Zhang

  2. Department of Radiation Oncology, 1st Affiliated Hospital of Nanchang University, Nanchang, China

    Changfei Gong

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  1. Zhongsheng Zou
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  2. Changfei Gong
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  3. Lingpeng Zeng
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  4. Yu Guan
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  7. Qiegen Liu
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  8. Minghui Zhang
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Contributions

Zhongsheng Zou: data curation, methodology, project administration, and writing—original draft. Changfei Gong: conceptualization, methodology, formal analysis, writing, review, and editing. Lingpeng Zeng: formal analysis and investigation. Yu Guan: investigation and project administration. Bin Huang: data curation and formal analysis. Xiuwen Yu: data curation and investigation. Qiegen Liu: conceptualization, formal analysis, and editing. Minghui Zhang: methodology, formal analysis, and writing—review and editing.

Corresponding authors

Correspondence to Qiegen Liu or Minghui Zhang.

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The study has been approved by the Institution Review Board and Ethics Committee of Jiangxi Cancer Hospital, and the approval number is 2022ky012.

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Zou, Z., Gong, C., Zeng, L. et al. Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. J Digit Imaging. Inform. med. 37, 60–71 (2024). https://doi.org/10.1007/s10278-023-00930-w

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