Analytical probabilistic treatment planning

• Applicants: Dr. Mark Bangert; Professor Dr. Philipp Hennig
• Subject Area: Medical Physics, Biomedical Technology
• Term: from 2014 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 265744405

We propose a three year research program extending our joint work regarding analytical probabilistic modeling (APM) for radiation therapy treatment planning.In current clinical practice, radiation therapy treatment planning does not explicitly model sources of uncertainty such as inter- and intrafractional motion, patient immobilization, and delineation errors. After geometric uncertainties have been reduced as far as possible through image guidance, uncertainties are only implicitly accounted for with standardized margin recipes. Mathematical approaches for patient specific uncertainty quantification and minimization have never found broad clinical application due to conceptual limitations and run time issues. Generic margin approaches compromise the quality of radiation treatments for individual patients. A uniform margin may cause unnecessary exposure of healthy tissue and the margin concept does not prevent "cold spots" within the tumor.We want to close this gap of inadequate clinical uncertainty modeling and develop APM into a computational research framework for probabilistic radiation therapy treatment planning for protons and carbon ions.APM enables the closed form computation of the expectation value and the (co-)variance of intensity-modulated dose distributions (and in principle all higher-order moments). This closed algebraic form provides central advantages for uncertainty quantification and minimization.1. The quality of all numerical simulations can be directly controlled and evaluated; APM is not compromised by algorithmic uncertainties, e.g. statistical fluctuations2. APM explicitly incorporates complex correlation models of the uncertainties and the non-trivial dosimetric interplay of random and systematic uncertainties in fractionated radiation therapy3. APM allows for the closed form definition, differentiation, and thence efficient optimization of existing and novel probabilistic objectives4. The output of APM is a Gaussian probability density which enables the propagation of uncertainty between related computationsWith the proposed research we pursue two goals:1. Exploit the reduction in computational complexity achieved through the analytical formulation of the treatment planning problem, to enable efficient robust planning of carbon ion treatments including uncertainties in the associated biological models.2. Enable closed-form propagation of uncertainty into composite treatment plan quality indicators. This will provide clinicians with error bars on quantities directly relevant for their decision and subsequently enable inverse planning through direct specification of probabilistic treatment plan features.Through accounting for potential discrepancies between the simulated and the actually delivered treatment plan we want to help reduce both local failure and normal tissue complication during radiation therapy.

• DFG Programme: Research Grants
• Cooperation Partners: Professor Dr. Christian Peter Karger; Privatdozent Dr. Florian Sterzing