Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws.
In a small number of special cases it may be necessary to estimate the radiation dose to organs far away from the target volume of a patient receiving radiotherapy, e.g. the dose to the gonads or to the fetus of a pregnant patient. Previously, for external beam radiotherapy a model was developed, which enabled this estimation with acceptable accuracy for the range of photon beam energies from cobalt-60 to 25 MV, independent of the make of the treatment machines, for generally used techniques and for a wide range of distances from the beam axis and field sizes [Van der Giessen and Hurkmans, Int. J. Radiat. Oncol. Biol. Phys. 27, 717-724 (1993); van der Giessen, ibid. 30, 1239-1246 (1994); to appear in Int. J. Radiat. Oncol. Biol. Phys.]. In this paper, results of measurements and Monte Carlo calculations are reported for evaluation of the dose at large distances from some brachytherapy sources. This has resulted in information on the behavior of the tissue attenuation factor T(r) for commonly used sources: Co-60, Ir-192, and Cs-137, in the range of distances up to 60 cm from the source. The influence of the size of the phantom was investigated. A mathematical model, described earlier by Kornelsen and Young, proved to yield the best fit to the results [Br. J. Radiol. 54, 136 (1981)]. Parameters for this model are derived for these isotopes. A few experiments in a Rando Alderson phantom are presented to support the measurements and the calculations.
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