WE-A-500-01: Quality Control of Lung SBRT: Minimizing Uncertainties From Simulation to Treatment.

Published

Journal Article

Stringent quality control is critical to the success of lung SBRT. However, uncertainties exist in each step of the lung SBRT procedure. Characterization of organ motion using 4DCT may not present true tumor motion excursion due to its low temporal resolution and patients' breathing irregularity. In treatment planning, tumor ITV could be significantly under/over-estimated by 4DCT. Different delivery techniques such as 3D-conformal, IMRT/VMAT, may introduce different degrees of uncertainties. The relative pros/cons of each motion management technique are usually not patient-specifically quantified, leading to uncertainty in finding the optimal technique for the patient. Other uncertainties include the choosing of planning CT, the determination of PTV and gating window, etc. At patient positioning, image guidance is prone to uncertainties due to technical limitations: CBCT may not present true ITV due to patient's irregular breathing. Rotational errors may not be fully corrected without six degree-of-freedom couch. At dose delivery, correlation between external motion and tumor motion is uncertain for respiratory-gated treatment. Tumor motion and size during treatment may be different from those measured at simulation, and may vary between fractions. For physics QAs, uncertainties in dose measurement devices and delivery devices could be substantial with inhomogeneity tissues. Quality control of lung SBRT requires accurate quantification of target motion by using the proper 4D-imaging technique and parameters based on patient's conditions, quantitative evaluation of different planning techniques, QA of treatment plan using 4D dosimeter and 4D phantom simulations, optimal patient positioning and motion monitoring during treatments. LEARNING OBJECTIVES: 1. Provide an evidence-based systematic review of uncertainties during lung SBRT 2. Discuss the root causes of the uncertainties and corresponding quality control strategies 3. Present data-driven practical and effective solutions to minimize the uncertainties.

Full Text

Duke Authors

Cited Authors

  • Yin, F; Benedict, S; Bradley, J; Cai, J; Wijesooriya, K

Published Date

  • June 2013

Published In

Volume / Issue

  • 40 / 6Part28

Start / End Page

  • 464 -

PubMed ID

  • 28519909

Pubmed Central ID

  • 28519909

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1118/1.4815490

Language

  • eng

Conference Location

  • United States