Skip to main content

Advertisement

Springer Nature Link
Log in

An inverse planning simulated annealing algorithm with adaptive weight adjustment for LDR pancreatic brachytherapy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The inverse planning simulated annealing (IPSA) algorithm has shown good results in cancer surgical treatment planning. However, an adaptive approach has not well been proposed for different shapes and sizes of tumors. The purpose of this study was to propose an adaptive, efficient and safe algorithm to get high-quality treatment dose planning, which is presented for pancreatic cancer.

Methods

An algorithm employs an optimized IPSA and an adaptive process for adjusting the weight of organs at risk (OAR) and tumor. The algorithm, which was combined with ant colony optimization, was further optimized to reduce the number of needles. It could meet the clinical dose objectives within the tumors, reduce the dose distribution within the OAR and minimize the number of needles. Ten clinical cases were chosen randomly from patients, previously successfully treated in clinic to test our method. The algorithm was validated against clinical cases, using clinically relevant dose parameters.

Results

The results were compared with clinical results in ten cases, indicating that the dose distribution within the tumor meets the clinical dose objectives. The dose received by OAR had been greatly reduced, and the number of needles could be reduced by about 50%. It was a significant improvement over the clinical treatment planning.

Conclusions

In this paper, we have devised an algorithm to optimize the treatment planning in brachytherapy. The method in this paper could meet the clinical dose objectives and reduce the difficulty of operation. The results were clinically acceptable. This algorithm is also applicable to other cancers such as lung cancer.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Kollmeier MA, Stock RG, Stone N (2003) Biochemical outcomes after prostate brachytherapy with 5-year minimal follow-up: Importance of patient selection and implant quality. Int J Radiat Oncol 57(3):645–653

    Article  CAS  Google Scholar 

  2. de la Puente P, Azab AK (2014) Delivery systems for brachytherapy. J Control Release 192:19–28

    Article  Google Scholar 

  3. Nath R, Andseson LL, Luxton G, Weaver KA, Williamson JF, Meigonni AS (1995) Dosimetry of interstitial brachytherapy sources-recommendations of the AAPM radiation-therapy committee task group NO 43. Med Phys 22(2):209–234

    Article  CAS  Google Scholar 

  4. Rivard MJ, Coursey BM, DeWerd LA, Hanson WF, Huq MS, Ibbott GS, Mitch MG, Nath R, Williamson JF (2004) Update of AAPM Task Group No 43 Report: a revised AAPM protocol for brachytherapy dose calculations. Med Phys 31(3):633–674

    Article  Google Scholar 

  5. Beaulieu L, Tedgren AC, Carrier JF, Davis SD, Mourtada F, Rivard MJ, Thomson RM, Verhaegen F, Wareing TA, Williamson JF (2012) Report of the Task Group 186 on model-based dose calculation methods in brachytherapy beyond the TG-43 formalism: current status and recommendations for clinical implementation. Med Phys 39(10):6208–6236

    Article  Google Scholar 

  6. Holm HH, Juul N, Pedersen JF, Hansen H, Stroyer I (1983) Transperineal I125 seed implantation in prostatic cancer guided by trans-rectal ultrasonography. J Urology 130(2):283–286

    Article  CAS  Google Scholar 

  7. Blasko JC, Mate T, Sylvester JE, Grimm PD, Cavanagh W (2002) Brachytherapy for carcinoma of the prostate: techniques, patient selection, and clinical outcomes. Semin Radlat Oncol 12(1):81–94

    Article  Google Scholar 

  8. Du P, Xiao YY, Lu W (2017) Modified fan-shaped distribution technology for computed tomography (CT)-Guided radioactive seed implantation in lung cancer patients with lung dysfunction. Med Sci Monitor 23:4366–4375

    Article  Google Scholar 

  9. Fan JW, Wang JZ, Chen Z, Hu CS, Zhan Z, Hu WG (2019) Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique. Med Phys 46(1):370–381

    Article  Google Scholar 

  10. Nguyen D, Long T, Jia X, Lu WG, Gu XJ, Iqbal Z, Jiang S (2019) A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep-UK 9:1076

    Article  Google Scholar 

  11. Nguyen D, Barkousaraie AS, Shen CY, Jia X, Jiang S (2019) Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning. MICCAI 2019 Lecture Notes in Computer Science 11769:59–67

  12. Mao X, Pineau J, Keyes R, Enger SA (2020) RapidBrachyDL: rapid radiation dose calculations in brachytherapy via deep learning. Int J Radiat Oncol Biol Phys 108(3):802–812. https://doi.org/10.1016/j.ijrobp.2020.04.045

    Article  PubMed  Google Scholar 

  13. Villa M, Bert J, Valeri A, Schick U, Visvikis D (2021) Fast monte carlo-based inverse planning for prostate brachytherapy by using deep learning. IEEE Trans Radiat Plasma Med Sci. https://doi.org/10.1109/TRPMS.2021.3060191

    Article  Google Scholar 

  14. Wang ZM, Lu J, Liu T, Chen KM, Huang G, Liu FJ (2011) CT-guided interstitial brachytherapy of inoperable non-small cell lung cancer. Lung Cancer 74(2):253–257

    Article  Google Scholar 

  15. Jiang YR, Sykes ER (2015) A 3D computer-assisted treatment planning system for breast cancer brachytherapy treatment. Int J Comput Ass Rad 10(4):373–381

    Google Scholar 

  16. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  CAS  Google Scholar 

  17. Lessard E, Pouliot J (2001) Inverse planning anatomy-based dose optimization for HDR-brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function. Med Phys 28(5):773–779

    Article  CAS  Google Scholar 

  18. Alterovitz R, Lessard E, Pouliot J, Hsu ICJ, O’Brien JF, Goldberg K (2006) Optimization of HDR brachytherapy dose distributions using linear programming with penalty costs. Med Phys 33(11):4012–4019

    Article  Google Scholar 

  19. Poulin E, Fekete CAC, Letourneau M, Fenster A, Pouliot J, Beaulieu L (2013) Adaptation of the CVT algorithm for catheter optimization in high dose rate brachytherapy. Med Phys 40(11):111724

    Article  Google Scholar 

  20. Mountris KA, Visvikis D, Bert J (2019) DVH-Based inverse planning using Monte Carlo dosimetry for LDR prostate brachytherapy. Int J Radiat Oncol Biol Phys 103(2):503–510. https://doi.org/10.1016/j.ijrobp.2018.09.041

    Article  PubMed  Google Scholar 

  21. Woudstra E, Heijmen BJM (2003) Automated beam angle and weight selection in radiotherapy treatment planning applied to pancreas tumors. Int J Radiat Oncol 56(3):878–888

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant Number 81871457) and National Natural Science Foundation of China (Grant Number 8167071354).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China

    Ruijin Zhang, Zhiyong Yang, Shan Jiang, Zeyang Zhou & Guobin Zhang

  2. Department of Interventional Ultrasound, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China

    Xiaoling Yu & Erpeng Qi

Authors
  1. Ruijin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiyong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoling Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erpeng Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zeyang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guobin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shan Jiang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Yang, Z., Jiang, S. et al. An inverse planning simulated annealing algorithm with adaptive weight adjustment for LDR pancreatic brachytherapy. Int J CARS 17, 601–608 (2022). https://doi.org/10.1007/s11548-021-02483-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-021-02483-1

Keywords