Advances in Technology and Its Application: Radiotherapy Treatment Planning

Treatment planning for radiotherapy has become unthinkable without computer algorithms for dose optimization. Although the need for optimization algorithms originated from the complexity of treatment delivery technology such as intensity-modulated radiotherapy, volumetric-modulated arc therapy, and robotic stereotactic radiotherapy, the focus has shifted to refining goals and methods of optimization itself. Dose optimization chiefly advances in 3 directions: human interface and automation, compensation of changing patient geometries, and diversification/individualization of radiation dose prescription. Traditionally, dose optimization requires the definition of numerical treatment goals, followed by an interactive trial-and-error process to adjust the correct, patient-specific balance of these goals. Being both operator dependent and time consuming, methods are needed that produce high-quality treatments efficiently, with the long-term objective of autonomous dose optimization. Expedient treatment planning is also key to treatment adaptation to changes in patient geometry. Despite all efforts to image and adapt at treatment time, some residual uncertainties remain and must be compensated via treatment planning. Reformulations of the dose optimization problem are joined with various image-based 4D patient models to ensure treatment robustness against geometric uncertainties. Robust optimization leads to a deviation from customary dose prescription in favour of more predictable dose delivery. The dose distribution can be individualized further by additional functional image information, aiming to guide the dose towards undertreated volumes and away from overtreated ones, also known as dose-painting. Multimodal imaging is increasingly integrated into treatment planning, making it a natural consequence to supplant computed tomography by magnetic resonance imaging to establish MR-based radiotherapy.