Thermoradiotherapy Optimization Strategies Accounting for Hyperthermia Delivery Uncertainties

Abstract

IntroductionThe combined effect of hyperthermia and radiotherapy can be quantified by an enhanced equivalent radiation dose (EQDRT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQDRT. We developed and compared strategies for EQDRT optimization of radiotherapy plans, focusing on robustness against common adjustments. MethodsUsing Plan2Heat, we computed pre-planning hyperthermia plans and treatment adjustment scenarios for three cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with four different strategies: (1) Conventional radiotherapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) Nominal EQDRT optimization using the pre-planning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk (OAR) doses as in plan (1), (3) Robust EQDRT optimization, as (2) but adding adjusted scenarios for optimization, (4) Library of Plans (four plans), with strategy (2) criteria but optimizing on one adjusted scenario per plan. We calculated for each radiotherapy plan EQDRT distributions for pre-planning and adjusted scenarios, evaluating for each combination GTV coverage and homogeneity objectives. ResultsEQDRT95% increased from 49.9-50.9 Gy in strategy (1) to 56.1-57.4 Gy in strategy (2) with the pre-planning scenario, improving homogeneity in ∼10%. Strategy (2) demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy (3) had higher percentage of coverage objectives within tolerance than strategy (2) (68% vs 54%), but lower percentage for uniformity (44% vs 71%). Strategy (4) showed similar EQDRT95% and homogeneity for adjusted scenarios than strategy (2) for pre-planning scenario. D0.1% for OARs was increased by strategies (2-4) by up to ∼6 Gy. ConclusionsEQDRT optimization enhances EQDRT levels and uniformity compared to conventional optimization. Better overall robustness is achieved optimizing on the pre-planning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.