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Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate. To improve the dose accuracy on the tissue margin area for AAA, we proposed a novel deep learning-based dose accuracy improvement method using Margin-Net combined with Margin-Loss.

Methods

A novel model ‘Margin-Net’ was designed with a Margin Attention Mechanism to generate special margin-related features. Margin-Loss was introduced to consider the dose errors and dose gradients in tissues margin area. Ninety-five VMAT cervical cancer cases with paired AAA and AXB dose were enrolled in our study: 76 cases for training and 19 cases for testing. Tissues Margin Masks were generated from RT contours with 6 mm extension. Tissues Margin Mask, AAA dose and CTs were input data; AXB dose was used as reference dose for model training and evaluation. Comparison experiments were performed to evaluated effectiveness of Margin-Net and Margin-Loss.

Results

Compared to AXB dose, the 3D gamma passing rate (1%/1 mm, 10% threshold) for 19 test cases 95.75 ± 1.05% using Margin-Net with Margin-Loss, which was significantly higher than the original AAA dose (73.64 ± 3.46%). The passing rate reduced to 94.07 ± 1.16% without Margin-Loss and 87.3 ± 1.18% if Margin-Net key structure ‘MAM’ was also removed.

Conclusion

The proposed novel tissues margin-based dose conversion method can significantly improve the dose accuracy of Analytical Anisotropic Algorithm to be comparable to AXB algorithm. It can potentially improve the efficiency of treatment planning process with low demanding of computation resources.

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Acknowledgements

We sincerely thank the editor and all reviewers for their very insightful comments and constructive suggestions, which really helped us improve the quality of this manuscript.

Funding

Funding Program: The China National Key R&D Program during the 13th Five-year Plan Period (Grant Nos. 2016YFC0105206, 2016YFC0105207).

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Author notes

  1. Bo Yang and Yaoying Liu contributed equally to this work.

Authors and Affiliations

  1. Peking Union Medical College Hospital, Beijing, 100005, China

    Bo Yang, Zhiqun Wang & Jie Qiu

  2. School of Physics, Beihang University, Beijing, 102206, China

    Yaoying Liu

  3. Manteia Technologies Co., Ltd, Xiamen, 361008, China

    Yaoying Liu, Zhaocai Chen & Qichao Zhou

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  1. Bo Yang
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Correspondence to Qichao Zhou or Jie Qiu.

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Yang, B., Liu, Y., Chen, Z. et al. Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer. Int J CARS 18, 953–959 (2023). https://doi.org/10.1007/s11548-022-02801-1

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  • DOI: https://doi.org/10.1007/s11548-022-02801-1

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