Dealing with geometric uncertainties in dose painting by numbers: introducing the ΔVH

Dose painting by numbers lacks the conventional margin approach for geometric uncertainties. Moreover, the DVH is unable to assess the geometric accuracy of a non-uniform dose distribution because spatial information is lost. In this work we present tools for planning and evaluation of non-uniform treatment dose which take geometric uncertainties into account. The IMRT optimization functions in the Pinnacle treatment planning software were extended to allow non-uniform prescription dose distributions, e.g., derived from a PET image set. Also, explicit handling of systematic and random geometric uncertainties was incorporated in the functions, enabling confidence level based probabilistic treatment planning. For plan evaluation the concept of ΔVH was introduced, which is the volume histogram of the difference between planned and prescribed doses. Probability distributions for ΔVH points were estimated using Monte Carlo methods. As a demonstration of these methods, two examples are presented; one plan for a lung cancer patient and one for a tumor in the head-and-neck region. Dose distributions were obtained using the PET SUV, while allowing for geometric uncertainties. Optimization was performed such that the ΔVH evaluation indicated a 90% confidence of having under-dosage less than 5% of prescription dose maximum in 99% of the tumor volume. This corresponds to the clinical target constraint for margin based planning with uniform dose prescription. Clinical treatment planning tools were extended to allow non-uniform prescription. For planning we introduced confidence level based probabilistic optimization with non-uniform target dose, while confidence levels of ΔVH points summarize the probability of proper target coverage