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This ACROP guideline achieved detailed recommendations in all aspects of SBRT implementation and practice, which will contribute to further standardization of SBRT for peripherally located early stage NSCLC.

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Abstract. Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of many structures and lymph node regions. Manual contouring is tedious and suffers from large inter-and intra-rater variability. To reduce manual labor, we have developed a fully automated, atlas-based method for H&N CT image segmentation that employs a novel hierarchical atlas registration approach. This registration strategy makes use of object shape information in the atlas to help improve the registration efficiency and robustness while still being able to account for large inter-subject shape differences. Validation results showed that our method provides accurate segmentation for many structures despite difficulties presented by real clinical data. Comparison of two different atlas selection strategies is also reported.

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a b s t r a c tBackground and purpose: A planning target volume (PTV) in photon treatments aims to ensure that the clinical target volume (CTV) receives adequate dose despite treatment uncertainties. The underlying static dose cloud approximation (the assumption that the dose distribution is invariant to errors) is problematic in intensity modulated proton treatments where range errors should be taken into account as well. The purpose of this work is to introduce a robustness evaluation method that is applicable to photon and proton treatments and is consistent with (historic) PTV-based treatment plan evaluations. Materials and methods: The limitation of the static dose cloud approximation was solved in a multiscenario simulation by explicitly calculating doses for various treatment scenarios that describe possible errors in the treatment course. Setup errors were the same as the CTV-PTV margin and the underlying theory of 3D probability density distributions was extended to 4D to include range errors, maintaining a 90% confidence level. Scenario dose distributions were reduced to voxel-wise minimum and maximum dose distributions; the first to evaluate CTV coverage and the second for hot spots. Acceptance criteria for CTV D98 and D2 were calibrated against PTV-based criteria from historic photon treatment plans. Results: CTV D98 in worst case scenario dose and voxel-wise minimum dose showed a very strong correlation with scenario average D98 (R 2 > 0.99). The voxel-wise minimum dose visualised CTV dose conformity and coverage in 3D in agreement with PTV-based evaluation in photon therapy. Criteria for CTV D98 and D2 of the voxel-wise minimum and maximum dose showed very strong correlations to PTV D98 and D2 (R 2 > 0.99) and on average needed corrections of À0.9% and +2.3%, respectively. Conclusions: A practical approach to robustness evaluation was provided and clinically implemented for PTV-less photon and proton treatment planning, consistent with PTV evaluations but without its static dose cloud approximation. Ó 2019 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 141 (2019) 267-274 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).The use of margins in photon radiotherapy is a long established and universally adopted method to provide adequate target coverage under the presence of uncertainties. The CTV-PTV margin provides a geometrical buffer zone around the target within which the desired dose is achieved for the majority of treatments; criteria of 95% of the prescription dose in 90% of the patient population has found general appeal [1,2]. The suitability of a geometricallyexpanded buffer zone arises from the (relative) insensitivity of megavoltage photon dose distributions to density changes in the beam path. By and large, the biggest risk to a photon dose distribution is a geometrical miss -a translation of the CTV relative to the beam. Therefore, the static dose cloud approximation (dose distribution is invariant to errors)...