Abstract
Prior information about patient status and previously archived treatment plans, particularly if performed by expert clinicians, could be used to inform the treating team of a current pending case. This notion of using prior treatment planning information constitutes the underlying principle of the so-called knowledge-based treatment planning (KBTP). In this chapter, we will discuss KBTP and provide some examples highlighting its current status, the role of machine learning, and its potential for decision support in radiotherapy.
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References
Videtic GMM, Woody N, Vassil AD. Handbook of treatment planning in radiation oncology. 2nd ed. New York: Demos Medical; 2015.
Khan FM. Treatment planning in radiation oncology. 2nd ed. Philadelphia: Lippincott Williams & Wilkins; 2007.
Hodapp N. The ICRU Report 83: prescribing, recording and reporting photon-beam intensity-modulated radiation therapy (IMRT). Strahlenther Onkol Organ der Deutschen Rontgengesellschaft. 2012;188:97–9.
Chanyavanich V, Das SK, Lee WR, Lo JY. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011;38:2515–22.
Moore KL, Brame RS, Low DA, Mutic S. Experience-based quality control of clinical intensity-modulated radiotherapy planning. Int J Radiat Oncol Biol Phys. 2011;81:545–51.
Good D, Lo J, Lee WR, Wu QJ, Yin FF, Das SK. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. Int J Radiat Oncol Biol Phys. 2013;87:176–81.
Zhang HH, D’Souza WD, Shi L, Meyer RR. Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys. 2009;74:1617–26.
Appenzoller LM, Michalski JM, Thorstad WL, Mutic S, Moore KL. Predicting dose-volume histograms for organs-at-risk in IMRT planning. Med Phys. 2012;39:7446–61.
Petrovic S, Mishra N, Sundar S. A novel case based reasoning approach to radiotherapy planning. Expert Syst Appl. 2011;38:10759–69.
Yager RR, Liu L. Classic works of the Dempster-Shafer theory of belief functions. Berlin/New York: Springer; 2008.
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El Naqa, I. (2015). Knowledge-Based Treatment Planning. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_10
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DOI: https://doi.org/10.1007/978-3-319-18305-3_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18304-6
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