PURPOSE: This paper studies the feasibility of using Monte Carlo (MC) for treatment planning of intraoperative electron radiation therapy (IOERT) procedure to get 3D dose by using patient's CT images. METHODS: The IOERT treatment planning was performed using the following successive steps: I) The Mobetron 1000® machine was modelled with the EGSnrc MC codes. II) The MC model was validated with measurements of percentage depth doses and profiles for three energies (12, 9, 6) MeV. III) CT images were imported as DICOM files. IV) Contouring of the planning target volume (PTV) and the organs at risk was done by the radiation oncologist. V) The medical physicist with the radiation oncologist, had chosen the same parameters of IOERT procedures like energy, applicator (type, size) and using or not bolus. VI) Finally, dose calculation and analysis of 3D maps was carried out. RESULTS: The tuning process of the MC model provides good results, as the maximum value of the root mean square deviation (RMSD) was less than 3% between the MC simulated PDDs and the measured PDDs. The contouring and dose analysis review were easy to conduct for the classical treatment planning system. The radiation oncologist had many tools for dose analysis such as DVH and color wash for all the slides. Summation of the 3D dose of IOERT with other radiotherapy plans is possible and helpful for total dose estimation. Archiving and documentation is as good as treatment planning system (TPS). CONCLUSIONS: The method displayed in this paper provides a step forward for IOERT Dosimetry and allows to obtain accurate dosimetry of treated volumes.
PURPOSE: This paper studies the feasibility of using Monte Carlo (MC) for treatment planning of intraoperative electron radiation therapy (IOERT) procedure to get 3D dose by using patient's CT images. METHODS: The IOERT treatment planning was performed using the following successive steps: I) The Mobetron 1000® machine was modelled with the EGSnrc MC codes. II) The MC model was validated with measurements of percentage depth doses and profiles for three energies (12, 9, 6) MeV. III) CT images were imported as DICOM files. IV) Contouring of the planning target volume (PTV) and the organs at risk was done by the radiation oncologist. V) The medical physicist with the radiation oncologist, had chosen the same parameters of IOERT procedures like energy, applicator (type, size) and using or not bolus. VI) Finally, dose calculation and analysis of 3D maps was carried out. RESULTS: The tuning process of the MC model provides good results, as the maximum value of the root mean square deviation (RMSD) was less than 3% between the MC simulated PDDs and the measured PDDs. The contouring and dose analysis review were easy to conduct for the classical treatment planning system. The radiation oncologist had many tools for dose analysis such as DVH and color wash for all the slides. Summation of the 3D dose of IOERT with other radiotherapy plans is possible and helpful for total dose estimation. Archiving and documentation is as good as treatment planning system (TPS). CONCLUSIONS: The method displayed in this paper provides a step forward for IOERT Dosimetry and allows to obtain accurate dosimetry of treated volumes.
Authors: Verónica García-Vázquez; Felipe A Calvo; María J Ledesma-Carbayo; Claudio V Sole; José Calvo-Haro; Manuel Desco; Javier Pascau Journal: PLoS One Date: 2020-01-10 Impact factor: 3.240