Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Wulf J, Baier K, Mueller G, Flentje MP (2005) Dose-response in stereotactic irradiation of lung tumors. Radiother Oncol 77(1):83–87. https://doi.org/10.1016/j.radonc.2005.09.003

    Article  PubMed  Google Scholar 

  2. Partridge M, Ramos M, Sardaro A, Brada M (2011) Dose escalation for non-small cell lung cancer: analysis and modelling of published literature. Radiother Oncol 99(1):6–11. https://doi.org/10.1016/j.radonc.2011.02.014

    Article  PubMed  Google Scholar 

  3. Sievinen J, Ulmer W, Kaissl W (2005) AAA photon dose calculation model in Eclipse. Palo Alto (CA): Varian Medical Systems. 118:2894.

  4. Van Esch A, Tillikainen L, Pyykkonen J, Tenhunen M, Helminen H, Siljamäki S, Alakuijala J, Paiusco M, Iori M, Huyskens DP (2006) Testing of the analytical anisotropic algorithm for photon dose calculation. Med Phys 33(11):4130–4148. https://doi.org/10.1118/1.2358333

    Article  PubMed  Google Scholar 

  5. Robinson D (2008) Inhomogeneity correction and the analytic anisotropic algorithm. J Appl Clin Med Phys 9(2):112–122. https://doi.org/10.1120/jacmp.v9i2.2786

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bragg CM, Conway J (2006) Dosimetric verification of the anisotropic analytical algorithm for radiotherapy treatment planning. Radiother Oncol 81(3):315–323. https://doi.org/10.1016/j.radonc.2006.10.020

    Article  PubMed  Google Scholar 

  7. Xing Y, Zhang Y, Nguyen D, Lin MH, Lu W, Jiang S (2020) Boosting radiotherapy dose calculation accuracy with deep learning. J Appl Clin Med Phys 21(8):149–159. https://doi.org/10.1002/acm2.12937

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ma C-M, Li J, Pawlicki T, Jiang S, Deng J, Lee M, Koumrian T, Luxton M, Brain S (2002) A Monte Carlo dose calculation tool for radiotherapy treatment planning. Phys Med Biol 47(10):1671. https://doi.org/10.1088/0031-9155/47/10/305

    Article  CAS  PubMed  Google Scholar 

  9. Failla GA, Wareing T, Archambault Y, Thompson S (2010) Acuros XB advanced dose calculation for the Eclipse treatment planning system. Varian Med Syst 20:18

    Google Scholar 

  10. Kan MW, Yu PK, Leung LH (2013) A review on the use of grid-based Boltzmann equation solvers for dose calculation in external photon beam treatment planning. BioMed Res Int. https://doi.org/10.1155/2013/692874.

  11. Rana S, Rogers K (2013) Dosimetric evaluation of Acuros XB dose calculation algorithm with measurements in predicting doses beyond different air gap thickness for smaller and larger field sizes. J Med Phys/Assoc Med Physicists India 38(1):9. https://doi.org/10.4103/0971-6203.106600

    Article  Google Scholar 

  12. Dong P, Xing L (2020) Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy. Phys Med Biol 65(3):035010. https://doi.org/10.1088/1361-6560/ab652d

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wu C, Nguyen D, Xing Y, Montero A, Schuemannn J, Shang H, Pu Y, Jiang S (2021) Improving proton dose calculation accuracy by using deep learning. Mach Learn Sci Technol 2(1):015017. https://doi.org/10.1088/2632-2153/abb6d5

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sumida I, Magome T, Das IJ, Yamaguchi H, Kizaki H, Aboshi K, Yamaguchi H, Seo Y, Isohashi F, Ogawa K (2020) A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy. Phys Med 72:88–95. https://doi.org/10.1016/j.ejmp.2020.03.023

    Article  PubMed  Google Scholar 

  15. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV) 2018. https://doi.org/10.48550/arXiv.1807.06521.

  16. Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp 1–2. https://doi.org/10.1109/IWQoS.2018.8624183.

  17. Nguyen D, Jia X, Sher D, Lin MH, Iqbal Z, Liu H, Jiang S (2019) 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture. Phys Med Biol 64(6):065020. https://doi.org/10.1088/1361-6560/ab039b

    Article  PubMed  Google Scholar 

  18. Pieper S, Halle M, Kikinis R (2004) 3D Slicer. Paper presented at: 2004 2nd IEEE international symposium on biomedical imaging: nano to macro (IEEE Cat No. 04EX821) 2004. https://doi.org/10.1109/ISBI.2004.1398617.

  19. Kry SF, Feygelman V, Balter P, Knöös T, Charlie Ma C-M, Snyder M, Tonner B, Vassiliev ONAAPM (2020) Task Group 329: Reference dose specification for dose calculations: Dose-to-water or dose-to-muscle? Med Phys 47:e52–e64. https://doi.org/10.1002/mp.13995

    Article  PubMed  Google Scholar 

  20. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. https://doi.org/10.1007/978-3-319-24574-4_28.

  21. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2016.90

  22. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.48550/arXiv.1608.06993.

  23. Liu Y, Chen Z, Wang J, Wang X, Qu B, Ma L, Zhao W, Zhang G, Xu S (2021) Dose prediction using a three-dimensional convolutional neural network for nasopharyngeal carcinoma with tomotherapy. FrontiOncol, p 11. https://doi.org/10.3389/fonc.2021.752007.

Download references

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).

Author information

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

Authors
  1. Bo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaoying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaocai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiqun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qichao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Qichao Zhou or Jie Qiu.

Ethics declarations

Conflict of interest

There was no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation