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Papp, D

Publications and source records attributed to Papp, D.

TH-E-BRF-01: Exploiting Tumor Shrinkage in Split-Course Radiotherapy

Purpose: In split-course radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated by radiobiological considerations. However, using modern image-guidance, it also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. In this work, we consider the optimal design of split-course treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. Methods: We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cell repopulation. The design of splitcourse radiotherapy is formulated as a mathematical optimization problem in which the total dose to the liver is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. Results: We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one third of the dose should be delivered in the first stage. The projected benefit of split-course treatments in terms of liver sparing depends on model assumptions. However, the model predicts large liver dose reductions by more than a factor of two for plausible model parameters. Conclusion: The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at split-course radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.

60 APPLIED LIFE SCIENCES↗

SU-E-T-478: Sliding Window Multi-Criteria IMRT Optimization

Purpose: To demonstrate a method for what-you-see-is-what-you-get multi-criteria Pareto surface navigation for step and shoot IMRT treatment planning. Methods: We show mathematically how multiple sliding window treatment plans can be averaged to yield a single plan whose dose distribution is the dosimetric average of the averaged plans. This is incorporated into the Pareto surface navigation based approach to treatment planning in such a way that as the user navigates the surface, the plans he/she is viewing are ready to be delivered (i.e. there is no extra ‘segment the plans’ step that often leads to unacceptable plan degradation in step and shoot Pareto surface navigation). We also describe how the technique can be applied to VMAT. Briefly, sliding window VMAT plans are created such that MLC leaves paint out fluence maps every 15 degrees or so. These fluence map leaf trajectories are averaged in the same way the static beam IMRT ones are. Results: We show mathematically that fluence maps are exactly averaged using our leaf sweep averaging algorithm. Leaf transmission and output factor corrections effects, which are ignored in this work, can lead to small errors in terms of the dose distributions not being exactly averaged even though the fluence maps are. However, our demonstrations show that the dose distributions are almost exactly averaged as well. We demonstrate the technique both for IMRT and VMAT. Conclusions: By turning to sliding window delivery, we show that the problem of losing plan fidelity during the conversion of an idealized fluence map plan into a deliverable plan is remedied. This will allow for multicriteria optimization that avoids the pitfall that the planning has to be redone after the conversion into MLC segments due to plan quality decline. David Craft partially funded by RaySearch Laboratories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SU-E-T-549: A Combinatorial Optimization Approach to Treatment Planning with Non-Uniform Fractions in Intensity Modulated Proton Therapy

Purpose: Non-uniform fractionation, i.e. delivering distinct dose distributions in two subsequent fractions, can potentially improve outcomes by increasing biological dose to the target without increasing dose to healthy tissues. This is possible if both fractions deliver a similar dose to normal tissues (exploit the fractionation effect) but high single fraction doses to subvolumes of the target (hypofractionation). Optimization of such treatment plans can be formulated using biological equivalent dose (BED), but leads to intractable nonconvex optimization problems. We introduce a novel optimization approach to address this challenge. Methods: We first optimize a reference IMPT plan using standard techniques that delivers a homogeneous target dose in both fractions. The method then divides the pencil beams into two sets, which are assigned to either fraction one or fraction two. The total intensity of each pencil beam, and therefore the physical dose, remains unchanged compared to the reference plan. The objectives are to maximize the mean BED in the target and to minimize the mean BED in normal tissues, which is a quadratic function of the pencil beam weights. The optimal reassignment of pencil beams to one of the two fractions is formulated as a binary quadratic optimization problem. A near-optimal solution to this problem can be obtained by convex relaxation and randomized rounding. Results: The method is demonstrated for a large arteriovenous malformation (AVM) case treated in two fractions. The algorithm yields a treatment plan, which delivers a high dose to parts of the AVM in one of the fractions, but similar doses in both fractions to the normal brain tissue adjacent to the AVM. Using the approach, the mean BED in the target was increased by approximately 10% compared to what would have been possible with a uniform reference plan for the same normal tissue mean BED.

61 RADIATION PROTECTION AND DOSIMETRY↗