RESUMO
The future of radiation oncology is exceptionally strong as we are increasingly involved in nearly all oncology disease sites due to extraordinary advances in radiation oncology treatment management platforms and improvements in treatment execution. Due to our technology and consistent accuracy, compressed radiation oncology treatment strategies are becoming more commonplace secondary to our ability to successfully treat tumor targets with increased normal tissue avoidance. In many disease sites including the central nervous system, pulmonary parenchyma, liver, and other areas, our service is redefining the standards of care. Targeting of disease has improved due to advances in tumor imaging and application of integrated imaging datasets into sophisticated planning systems which can optimize volume driven plans created by talented personnel. Treatment times have significantly decreased due to volume driven arc therapy and positioning is secured by real time imaging and optical tracking. Normal tissue exclusion has permitted compressed treatment schedules making treatment more convenient for the patient. These changes require additional study to further optimize care. Because data exchange worldwide have evolved through digital platforms and prisms, images and radiation datasets worldwide can be shared/reviewed on a same day basis using established de-identification and anonymization methods. Data storage post-trial completion can co-exist with digital pathomic and radiomic information in a single database coupled with patient specific outcome information and serve to move our translational science forward with nimble query elements and artificial intelligence to ask better questions of the data we collect and collate. This will be important moving forward to validate our process improvements at an enterprise level and support our science. We have to be thorough and complete in our data acquisition processes, however if we remain disciplined in our data management plan, our field can grow further and become more successful generating new standards of care from validated datasets.
RESUMO
A deconvolution algorithm has been developed to obtain robust fluence for external beam radiation treatment under geometrical uncertainties. Usually, the geometrical uncertainty is incorporated in the dose optimization process for inverse treatment planning to determine the additional intensity modulation of the beam to counter the geometrical uncertainty. Most of these approaches rely on dose convolution which is subject to the error caused by patient surface curvature and internal inhomogeneity. In this work, based on an 1D deconvolution algorithm developed by Ulmer and Kaissl, a fluence-deconvolution approach was developed to obtain robust fluence through the deconvolution of the nominal static one given by any treatment planning system. It incorporates the geometrical uncertainty outside the dose optimization procedure and therefore avoids the error of dose convolution. Robust fluences were calculated for a 4 x 4 cm flat field, a prostate IMRT and a head and neck IMRT plan in a commercial treatment planning system. The corresponding doses were simulated for 30 fractions with the random Gaussian distribution of the iso-centers showing good agreement with the nominal static doses. The feasibility of this deconvolution approach for clinical IMRT planning has been demonstrated. Because it is separated from the optimization procedure, this method is more flexible and easier to integrate into different existing treatment planning systems to obtain robust fluence.
