1. Commissioning the neutron production of a Linac: Development of a simple tool for second cancer risk estimation
- Author
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S. Moral-Sánchez, Maria do Carmo Lopes, Carles Domingo, M. Melchor, C. Sandín, D. Grishchuk, Francisco Sánchez-Doblado, Brigida Costa Ferreira, Faustino Gómez, M. Romero-Expósito, Beatriz Sánchez-Nieto, and J.A. Terrón
- Subjects
Equivalent dose ,business.industry ,Detector ,Linearity ,General Medicine ,Linear particle accelerator ,Dosimetry ,Neutron detection ,Neutron ,Production (computer science) ,Nuclear medicine ,business ,Algorithm ,Mathematics - Abstract
Purpose: Knowing the contribution of neutron to collateral effects in treatments is both a complex and a mandatory task. This work aims to present an operative procedure for neutron estimates in any facility using a neutron digital detector. Methods: The authors’ previous work established a linear relationship between the total second cancer risk due to neutrons (TR{sup n}) and the number of MU of the treatment. Given that the digital detector also presents linearity with MU, its response can be used to determine the TR{sup n} per unit MU, denoted as m, normally associated to a generic Linac model and radiotherapy facility. Thus, from the number of MU of each patient treatment, the associated risk can be estimated. The feasibility of the procedure was tested by applying it in eight facilities; patients were evaluated as well. Results: From the reading of the detector under selected irradiation conditions, m values were obtained for different machines, ranging from 0.25 × 10{sup −4}% per MU for an Elekta Axesse at 10 MV to 6.5 × 10{sup −4}% per MU for a Varian Clinac at 18 MV. Using these values, TR{sup n} of patients was estimated in each facility and compared to that frommore » the individual evaluation. Differences were within the range of uncertainty of the authors’ methodology of equivalent dose and risk estimations. Conclusions: The procedure presented here allows an easy estimation of the second cancer risk due to neutrons for any patient, given the number of MU of the treatment. It will enable the consideration of this information when selecting the optimal treatment for a patient by its implementation in the treatment planning system.« less
- Published
- 2014
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