S. Van Hoof
Maastricht University
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Publication
Featured researches published by S. Van Hoof.
Radiotherapy and Oncology | 2016
Sanaz Yahyanejad; Henry King; Venus Sosa Iglesias; Patrick V. Granton; L. Barbeau; S. Van Hoof; Arjan J. Groot; Roger Habets; Jos Prickaerts; Anthony J. Chalmers; Jan Theys; Susan Short; Frank Verhaegen; Marc Vooijs
Purpose or Objective: The novel combretastatin analogue, OXi4503, is a vascular disrupting agent (VDA) that has recently been shown to significantly enhance a stereotactic radiation treatment. This was achieved using an OXi4503 dose of 10 mg/kg combined with a stereotactic treatment of 3 x 15 Gy. The current study was undertaken to determine the OXi4503 dose dependency when using different stereotactic radiation dose schedules.
Radiotherapy and Oncology | 2015
Marleen Balvert; S. Van Hoof; Patrick V. Granton; Daniela Trani; Dick den Hertog; A.L. Hoffmann; Frank Verhaegen
PO-0785 Inverse planning of beam-on times for precision imageguided 3D small animal radiotherapy treatments M. Balvert, S.J. Van Hoof, P.V. Granton, D. Trani, D. Den Hertog, A.L. Hoffmann, F. Verhaegen Center for Economic Research (CentER) Tilburg University, Econometric and Operations Research, Tilburg, The Netherlands Maastricht Radiation Oncology (MAASTRO Clinic), Physics Research, Maastricht, The Netherlands Purpose/Objective: Advances in small animal radiotherapy enable the delivery of increasingly complex heterogeneous dose distributions on the millimeter scale, but methods to plan complicated small animal treatments remain in their infancy. A pre-clinical irradiation plan is usually created based on cone beam CT data with the animal in treatment position under anesthesia. Combined with demands on throughput, fast and easy treatment planning methods and algorithms are required. The purpose of this study is to develop an optimization model that determines beam-on times for a given beam configuration, and to assess the benefits of automated treatment planning for small animal radiotherapy. Materials and Methods: The applied model determines a Pareto-optimal solution based on user-provided weights for objectives. An interactive approach allows the user to select the plan that yields the most preferred trade-offs. Two cases based on cone beam CT data of a rat were used, and manual and model-based optimization results were compared using dose-volume metrics. The kidneys, spine and gastrointestinal tract (GI) were delineated as organs at risk (OARs) and a fictitious planning target volume (PTV) was created around the spine. In case 1, the left kidney was targeted as PTV with four 10x10 mm beams and for case 2, twelve 8x10 mm beams were used to target the PTV around the spine. A PTV dose of 8 Gy was prescribed, with a mean dose between 8 and 10 Gy as constraint. Differences between prescribed and planned PTV dose, as well as OAR doses were included in penalty objectives. The model was integrated in a research version of Monte Carlo based small animal treatment planning system SmART-Plan (v2.0 Precision X-ray). Results: Results show that manual and automated treatment planning yields plans of similar quality as shown in the figure and table. A similar amount of time was needed for manual and model-based optimization. In this period, manual optimization generates a single plan, while a set of Paretooptimal plans is created with automated optimization, allowing for a more substantiated choice on trade-offs. Automated optimization often uses fewer beams than manual optimized plans, therewith lowering treatment delivery time. Additional benefits of automated planning include a decreased dependence on the planning skills of the user (often absent in pre-clinical research), and the potential to improve treatment standardization among institutions. For more complex irradiations, manual planning becomes infeasible, making automation a necessity.
Medical Physics | 2011
Patrick V. Granton; S. Van Hoof; Mark Podesta; S. Nijsten; W. Van Elmpt; Frank Verhaegen
Purpose: Novel small animal micro‐irradiators are becoming available for pre‐clinical research but they lack dedicated treatment planning systems. The purpose of this study is to develop both a Monte Carlo (MC) model and a portal dose prediction model of a small animal micro‐IR to enable forward dose calculations and compare the planned against the delivered treatment. Methods: A MC model of a small animal micro‐IR from the x‐ray tube assembly to the detector was developed. The model was compared to radiochromic film and portal images for validation. A portal dose calibration model was also developed and portal dose images were compared to film measurements. A rodent was irradiated with 1 Gy at 225 kVp (0.32 mm Cu) while portal images were acquired. A simulated portal image from the MC model was compared to the portal image acquired during irradiation. Results: Simulations of the MC models x‐ray spectra, beam profile, and half value layer thicknesses agreement within 2 % against measurements or external calculations. The portal dose prediction model resulted with 63% of pixels with a gamma value less than 1 for a gamma criterion of 5% 0.8 mm. A visual comparison between the simulated portal image and acquired portal image during irradiation of a rodent show good agreement but intensity values around regions of bone deviate from the acquired portal image. Conclusion: We have demonstrated that we can simulate the entire irradiation process of a small animal micro‐irradiator and generate comparable predicted portal images compared to acquired portal images. We believe that further refinement to the tissue and density assignment in the MC model will improve our results.
Medical Physics | 2014
Patricia Lindsay; Patrick V. Granton; A. Gasparini; S. Jelveh; R. Clarkson; S. Van Hoof; Jolanda Hermans; J. Kaas; F. Wittkamper; J.J. Sonke; Frank Verhaegen; David A. Jaffray
Radiotherapy and Oncology | 2018
Frank Verhaegen; S. Van Hoof; Lotte E J R Schyns; Sanaz Yahyanejad; B. Van der Heyden; Ana Vaniqui; Patrick V. Granton; L. Dubois; Marc Vooijs
Radiotherapy and Oncology | 2018
Lucas Persoon; S. Van Hoof; Patrick V. Granton; H. Beunk; J.W. Doosje; Frank Verhaegen
Radiotherapy and Oncology | 2016
Sanaz Yahyanejad; Henry King; Venus Sosa Iglesias; Patrick V. Granton; L. Barbeau; S. Van Hoof; Arjan J. Groot; Roger Habets; Jos Prickaerts; Anthony J. Chalmers; Susan Short; Frank Verhaegen; Jan Theys; Marc Vooijs
Radiotherapy and Oncology | 2016
Jan Theys; S. Van Hoof; V. Sosa Iglesias; L. Schijns; L. Barbeau; Natasja G. Lieuwes; L. Dubois; Frank Verhaegen; Marc Vooijs
Radiotherapy and Oncology | 2016
K. Panth; S. Carvalho; A. Yaromina; R.T.H. Leijenaar; S. Van Hoof; Natasja G. Lieuwes; B. Rianne; M. Granzier-Peeters; F. Hoebers; Daniëlle B.P. Eekers; M. Berbee; L. Dubois; P. Lambin
Radiotherapy and Oncology | 2015
Sanaz Yahyanejad; Patrick V. Granton; S. Van Hoof; L. Barbeau; Jan Theys; Frank Verhaegen; Marc Vooijs