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Background: Given the importance of scattered and low doses in secondary cancer caused by radiation treatment, the point dose of critical organs, which were not subjected to radiation treatment in breast cancer radiotherapy, was measured. Objective: The purpose of this study is to evaluate the peripheral dose in two techniques of breast cancer radiotherapy with two energies. Material and Methods: Eight different plans in two techniques (conventional and conformal) and two photon energies (6 and 15 MeV) were applied to Rando Alderson Phantom’s DICOM images. Nine organs were contoured in the treatment planning system and specified on the phantom. To measure the photon dose, fortyeight thermoluminescence dosimeters (MTS700) were positioned in special places on the above nine organs and plans were applied to Rando phantom with Elekta presice linac. To obtain approximately the same dose distribution in the clinical organ volume, a wedge was used on planes with an energy of 6 MeV photon. Results: Point doses in critical organs with 8 different plans demonstrated that scattering in low-energy photon is greater than high-energy photon. In contrast, neutron contamination in high-energy photon is not negligible. Using the wedge and shield impose greater scattering and neutron contamination on patients with low-and high-energy photon, respectively. Conclusion: Deciding on techniques and energies required for preparing an acceptable treatment plan in terms of scattering and neutron contamination is a key issue that may affect the probability of secondary cancer in a patient.

An increase in the demand of a new generation of radiotherapy planning systems based on learning approaches has been reported. At this stage, the new approach is able to improve the planning speed while saving a reasonable level of plan quality, compared with available planning systems. We believe that new achievements, such as deep-learning models, will be able to review the issue from a different point of view. The data of 120 breast cancer patients were used to train and test the three-dimensional U-Res-Net model. The network input was computed tomography images and patients' contouring, while the patients' dose distribution was addressed as the output of the model proposed. The predicted dose distributions, created by the model for 10 test patients, were then compared with corresponding dose distributions calculated by a reliable treatment planning system. In particular, the dice similarity coefficients for different isodose volumes, dose difference and mean absolute errors (MAE) for all voxels inside the body, D m e a n , D 98 % , D 50 % , D 2 % , V 95 % for planning target volume and organs at risk were calculated and were statistically analysed with the paired-samples t -test. The average dose difference for all patients and voxels in body was 0.60 ± 2.81%. The MAE varied from 3.85 ± 6.65% to 8.06 ± 10.00%. The average MAE for test cases was 5.71 ± 1.19%. The average dice similarity coefficients for isodose volumes was 0.91 ± 0.03. The three-dimensional gamma passing rates with 3 mm/3% criteria varied from 78.99% to 97.58% for planning target volume and organs at risk, respectively. The investigation showed that a deep-learning model can be applied to predict the three-dimensional dose distribution with optimal accuracy and precision for patients with left breast cancer. As further study, the model can be extended to predict dose distribution in other cancers. • A 3D deep-learning model was used to predict the dose distribution of a 3D conformal radiotherapy technique. • The proposed model has the potential to predict the 3D dose distribution faster than the treatment planning system. • This model can be used to predict the dose distribution of other cancers. [ABSTRACT FROM AUTHOR]