Treatment Planning for Stereotactic Body Radiation Therapy

  • Moyed Miften
  • Shiva K. Das
  • Indrin J. Chetty
  • David Westerly
Part of the Medical Radiology book series (MEDRAD)


Stereotactic body radiation therapy (SBRT) is a novel cancer treatment strategy where ultra-high doses per fraction are used, generally in the range of 8–20 Gy per fraction in a hypofractionated regimen of five or fewer fractions, to achieve a high biologically effective dose (BED). Such high doses per treatment were unimaginable in the past because of concerns about radiation-induced injury to organs at risk (OARs) if large volumes of OARs were irradiated to high doses. However, with recent technical advancements in highly conformal treatment planning, image-guided radiotherapy, and delivery technologies, it has become possible to safely deliver very large fractional doses of radiation to lesions in the lung, liver, spine, pancreas, and other disease sites. SBRT involves technically sophisticated treatment planning processes consisting of a chain of activities including: immobilization/simulation, motion management, imaging for treatment planning, segmentation, beam-setup and optimization, and plan verification. Clinicians are usually concerned of a possible compromise in treatment efficacy for any weak link in the planning chain process. A further cause for concern is the influence that errors or inaccuracies in the dose calculation may have on the treatment efficacy. Such errors arise from inherent limitations in the calculation algorithm used, which are more significant in some anatomical sites than others. In this chapter, treatment planning for linac-based delivery systems for lung, liver, spine, and pancreas is reviewed. In addition, the impact of the choice of calculation algorithm on the accuracy of dose distributions is discussed.


Monte Carlo Intensity Modulate Radiation Therapy Stereotactic Body Radiation Therapy Internal Target Volume Mean Lung Dose 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Moyed Miften
    • 1
  • Shiva K. Das
    • 2
  • Indrin J. Chetty
    • 3
  • David Westerly
    • 1
  1. 1.Department of Radiation OncologyUniversity of Colorado School of MedicineAuroraUSA
  2. 2.Department of Radiation OncologyDuke UniversityDurhamUSA
  3. 3.Department of Radiation OncologyHenry Ford Health SystemDetroitUSA

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