Thermoradiotherapy Optimization Strategies Accounting for Hyperthermia Delivery Uncertainties

The combined effect of hyperthermia and radiation therapy can be quantified by an enhanced equivalent radiation dose (EQD_RT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQD_RT. We developed and compared strategies for EQD_RT optimization of radiation therapy plans, focusing on robustness against common adjustments.

Using Plan2Heat, we computed preplanning hyperthermia plans and treatment adjustment scenarios for 3 cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with 4 different strategies: (1) conventional radiation therapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) nominal EQD_RT optimization using the preplanning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk doses as in plan 1, (3) robust EQD_RT optimization, as plan 2 but adding adjusted scenarios for optimization, and (4) library of plans (4 plans) with strategy 2 criteria but optimizing on 1 adjusted scenario per plan. We calculated for each radiation therapy plan EQD_RT distributions for preplanning and adjusted scenarios, evaluating each combination of GTV coverage and homogeneity objectives.

EQD_RT95% increased from 49.9 to 50.9 Gy in strategy 1 to 56.1 to 57.4 Gy in strategy 2 with the preplanning scenario, improving homogeneity by ∼10%. Strategy 2 demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy 3 had a higher percentage of coverage objectives within tolerance than strategy 2 (68% vs 54%) but a lower percentage for uniformity (44% vs 71%). Strategy 4 showed a similar EQD_RT95% and homogeneity for adjusted scenarios than strategy 2 for a preplanning scenario. D0.1% (radiation dose received by the 0.1% most irradiated volume) for organs at risk was increased by strategies 2 to 4 by up to ∼6 Gy.

EQD_RT optimization enhances EQD_RT levels and uniformity compared with conventional optimization. Better overall robustness is achieved by optimizing the preplanning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.