In this work we present a methodology able to use harmonized PET/CT imaging in dose painting by number (DPBN) approach by means of a robust and accurate treatment planning system. Image processing and treatment planning were performed by using a Matlab-based platform, called CARMEN, in which a full Monte Carlo simulation is included.
DPBN approach with our methodology was tested to reduce the uncertainties associated with both, the absolute value and the relative value of the information in the functional image. For the same H&N case, a single robust treatment was planned for dose prescription maps corresponding to standardized uptake value distributions from two different image reconstruction protocols: One to fulfill EARL accreditation for harmonization of [18F]FDG PET/CT image, and the other one to use the highest available spatial resolution.
Also, a robust treatment was planned to fulfill dose prescription maps corresponding to both approaches, the dose painting by contour based on volumes and our voxel-by-voxel DPBN. Adaptive planning was also carried out to check the suitability of our proposal. Different plans showed robustness to cover a range of scenarios for implementation of harmonizing strategies by using the highest available resolution.
Also, robustness associated to discretization level of dose prescription according to the use of contours or numbers was achieved. All plans showed excellent quality index histogram and quality factors below 2%. Efficient solution for adaptive radiotherapy based directly on changes in functional image was obtained. We proved that by using voxel-by-voxel DPBN approach it is possible to overcome typical drawbacks linked to PET/CT images, providing to the clinical specialist confidence enough for routinely implementation of functional imaging for personalized radiotherapy.
The results presented on Table 2 revealed that when the dose prescription map evaluated was the one based on BIOGRAPH protocol, P1 solution was always of superior quality, showing larger percentage of voxels within the tolerance for Q, as expected. Similarly, P2 showed the same trend when the map evaluated was the one based on the EARL protocol.
The robust solution P3 was slightly worse than P1 and P2 for BIOGRAPH and EARL protocols, respectively, but it was a good solution for both. The same can be said about QF. This robust solution satisfies the prescription based on the necessary harmonized image for multicenter studies and, simultaneously, fulfills the prescription corresponding to the available map with the highest resolution for the best biological target definition. In this work, a methodology able to implement the morpho-functional image dataset in the whole RT planning process, from the biological target definition up to the dose calculation and evaluation was developed.