Thermoradiotherapy Optimization Strategies Accounting for Hyperthermia Delivery Uncertainties.

Autor: Herrera TD; Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands; Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands. Electronic address: T.D.Herrera@amsterdamumc.nl., Ödén J; RaySearch Laboratories AB, Stockholm, Sweden., Lorenzo Polo A; RaySearch Laboratories AB, Stockholm, Sweden., Crezee J; Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands; Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands., Kok HP; Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands; Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: International journal of radiation oncology, biology, physics [Int J Radiat Oncol Biol Phys] 2024 Jul 15. Date of Electronic Publication: 2024 Jul 15.
DOI: 10.1016/j.ijrobp.2024.07.2146
Abstrakt: Purpose: 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.
Methods and Materials: 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.
Results: EQD RT 95% 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 RT 95% 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.
Conclusions: 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.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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