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Knowledge-Based Treatment Planning

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Machine and Deep Learning in Oncology, Medical Physics and Radiology

Abstract

Intensity-modulated radiation therapy (IMRT), including its variations (e.g., IMRT, volumetric arc therapy (VMAT), and tomotherapy), is a widely used and critically important technology for cancer treatment. IMRT treatments rely heavily on planning expertise due to its technical complexity and the conflicting nature of maximizing tumor control while minimizing normal organ damage. As treatment planning experience and especially the carefully designed clinical plan data being accumulated during the past two decades, a new set of technologies commonly termed knowledge-based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. In this chapter, we will first provide an overview of the concept of KBP—the problems it aims to address, the approaches that are effective, and the challenges it faces. Then we will present several KBP models, including a DVH prediction model, a whole breast radiation therapy (WBRT) fluence prediction model, and a beam angle bouquet model. These are a few samples from the vast amount of KBP literature published in the last years and represent some of the most recognized uses of KBP in clinical treatment planning practice.

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References

  1. Kalet IJ, Paluszynski W. Knowledge-based computer systems for radiotherapy planning. Am J Clin Oncol. 1990;13(4):344–51.

    Article  Google Scholar 

  2. Shwe MA, Tu SW, Fagan LM. Validating the knowledge base of a therapy planning system. Methods Inf Med. 1989;28(1):36–50.

    Article  Google Scholar 

  3. Zhang X, et al. A methodology for automatic intensity-modulated radiation treatment planning for lung cancer. Phys Med Biol. 2011;56(13):3873.

    Article  Google Scholar 

  4. Voet PW, et al. Toward fully automated multicriterial plan generation: a prospective clinical study. Int J Radiat Oncol Biol Phys. 2013;85(3):866–72.

    Article  Google Scholar 

  5. Hazell I, et al. Automatic planning of head and neck treatment plans. J Appl Clin Med Phys. 2016;17(1):272–82.

    Article  Google Scholar 

  6. Zhang J, et al. Knowledge-based tradeoff hyperplanes for head and neck treatment planning. Int J Radiat Oncol Biol Phys. 2020;106(5):1095–103.

    Article  Google Scholar 

  7. McIntosh C, Purdie TG. Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning. Phys Med Biol. 2016;62(2):415–31.

    Article  Google Scholar 

  8. Shiraishi S, Moore KL. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. Med Phys. 2016;43(1):378–87.

    Article  Google Scholar 

  9. Zhang J, et al. Voxel-level radiotherapy dose prediction using densely connected network with dilated convolutions. In: Artificial intelligence in radiation therapy. Cham: Springer International Publishing; 2019.

    Google Scholar 

  10. Nguyen D, et al. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture. Phys Med Biol. 2019;64(6):065020.

    Article  Google Scholar 

  11. Lee MS, et al. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep. 2019;9(1):10308.

    Article  ADS  Google Scholar 

  12. Lee H, et al. Fluence-map generation for prostate intensity-modulated radiotherapy planning using a deep-neural-network. Sci Rep. 2019;9(1):15671.

    Article  ADS  Google Scholar 

  13. Sheng Y, et al. Automatic planning of whole breast radiation therapy using machine learning models. Front Oncol. 2019;9:750.

    Article  Google Scholar 

  14. Li, X., et al. Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning method for real-time prostate treatment planning, in AAPM 61st Annual Meeting and Exhibition. St Antonio, Texas, USA; 2019.

    Google Scholar 

  15. Yuan L, et al. Standardized beam bouquets for lung IMRT planning. Phys Med Biol. 2015;60(5):1831–43.

    Article  Google Scholar 

  16. Yuan L, et al. Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT. Med Phys. 2014;41(2):021728.

    Article  Google Scholar 

  17. Sheng Y, et al. Outlier identification in radiation therapy knowledge-based planning: a study of pelvic cases. Med Phys. 2017;44(11):5617–26.

    Article  Google Scholar 

  18. Zhang J, et al. An ensemble approach to knowledge-based intensity-modulated radiation therapy planning. Front Oncol. 2018;8:57.

    Article  Google Scholar 

  19. Yuan L, et al. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. Med Phys. 2012;39(11):6868–78.

    Article  Google Scholar 

  20. Zhang J, et al. Modeling of multiple planning target volumes for head and neck treatments in knowledge-based treatment planning. Med Phys. 2019;46(9):3812–22.

    Article  Google Scholar 

  21. Tikhonov AN. On the stability of inverse problems. Comptes Rendus De L Academie Des Sciences De L Urss. 1943;39:176–9.

    MathSciNet  MATH  Google Scholar 

  22. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 2000;42(1):80–6.

    Article  Google Scholar 

  23. Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Series B Methodol. 1996;58(1):267–88.

    MathSciNet  MATH  Google Scholar 

  24. Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Stat Soc Ser B Stat Methodol. 2005;67:301–20.

    Article  MathSciNet  Google Scholar 

  25. Wolpert DH. Stacked generalization. Neural Netw. 1992;5(2):241–59.

    Article  Google Scholar 

  26. Tol JP, et al. Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys. 2015;91(3):612–20.

    Article  Google Scholar 

  27. Delaney AR, et al. Effect of dosimetric outliers on the performance of a commercial knowledge-based planning solution. Int J Radiat Oncol Biol Phys. 2016;94(3):469–77.

    Article  Google Scholar 

  28. LJP M, Hinton GE. Visualizint high-dimensional data using t-SNE. J Mach Learn Res. 2008;9:2579–605.

    Google Scholar 

  29. Wall PDH, Carver RL, Fontenot JD. Impact of database quality in knowledge-based treatment planning for prostate cancer. Pract Radiat Oncol. 2018;8(6):437–44.

    Article  Google Scholar 

  30. Boutilier JJ, et al. Sample size requirements for knowledge-based treatment planning. Med Phys. 2016;43(3):1212–21.

    Article  Google Scholar 

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Acknowledgments

This work is partially supported by an NIH grant (#R01CA201212) and a master research grant from Varian Medical Systems.

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Authors and Affiliations

  1. Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA

    Jiahan Zhang & Q. Jackie Wu

  2. College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, NC, USA

    Yaorong Ge

Authors
  1. Jiahan Zhang
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  2. Yaorong Ge
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  3. Q. Jackie Wu
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Corresponding author

Correspondence to Q. Jackie Wu .

Editor information

Editors and Affiliations

  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

  2. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA

    Martin J. Murphy

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Zhang, J., Ge, Y., Wu, Q.J. (2022). Knowledge-Based Treatment Planning. In: El Naqa, I., Murphy, M.J. (eds) Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer, Cham. https://doi.org/10.1007/978-3-030-83047-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-83047-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83046-5

  • Online ISBN: 978-3-030-83047-2

  • eBook Packages: MedicineMedicine (R0)

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