Presenter: Ben J. Heijmen Presenter's Affiliation: Daniel den Hoed Cancer Center, Rotterdam, The Netherlands Type of Session: Scientific
The current approach to IMRT involves a planning treatment volume (PTV)-based approach, which places a ?planning margin? around a physician-defined clinical target volume in order to account for both internal tumor motion and patient set-up error. For PTV margins that are in proximity to normal tissues at risk, somewhat smaller margins are selected a priori in an effort to better spare the normal organs, even at the expense of lowered PTV dose. The ICRU-62 report supports use of planning margins for organs at risk (OAR). The goal of this study is to create an algorithm for IMRT inverse treatment planning that does not depend on such arbitrarily-defined planning margins. The authors propose to eliminate the concept of PTV, and use a CTV-based plan instead. With this new method, population-based distributions are used to account for all geometrical uncertainties (tumor and normal organs), and beam fluence profiles are optimized.
Materials and Methods
Population mean dose volume histograms (mDVH) are calculated by taking weighted average of all possible combinations of random and systematic deviations from the treatment plan geometry
These mDVH's are then used to directly determine the CTV and OAR dose distribution changes that result from geometrical uncertainties.
Iterative optimization of fluence profiles is performed while accounting for geometric uncertainties and obeying imposed DVH constraints for CTV and OAR.
High dose regions were generated that were tightly outlined around critical organs and more generously located around portions of the CTV located farther away from OARs.
High standard was set : 99% of volume should receive at least 95% of the prescribed tumor dose.
Organ motion and deformation, as well as tumor delineation were all addressed with the algorithm.
Beam fluence profiles were optimized to find the ideal balance between dose delivery to CTV and dose sparing to normal tissue, all while accounting for geometric uncertainties.
This new algorithm for IMRT inverse treatment planning addresses issues of geometrical uncertainties stemming from set-up error, organ deformation, and tumor delineation by optimizing beam fluence profiles.
The concept of PTV-based treatment with planning margins is not needed with this method.
This integrated algorithm is a viable alternative to the use of a priori margins for critical tissues.
A fundamental shift in IMRT planning paradigms is presented here. The use of this integrated approach appears to achieve excellent tumor coverage while sparing normal organs at risk. Rather than focusing on planning margins that are often arbitrarily selected and tend to de-emphasize the importance of random errors in individual patients, this iterative method incorporates population-based data on all possible combinations of systematic and random errors, thereby providing a more extensive accounting of geometric uncertainties. Although 3D studies are needed to further support these data, this is a promising new approach for more accurate IMRT treatment delivery that could significantly change the way in which we define target volumes.
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