S1391
Interdisciplinary - Health economics & health services research
ESTRO 2026
Purpose/Objective: Modern radiation therapy has seen a large reduction of NTCP through technological advances and improved predictive modeling, paving the way toward hypofractionated treatments [1,2,3]. While it is natural to expect that hypofractionation will reduce waiting times by shortening treatment courses, this study simulates how much average patient waiting time can be reduced and increase machine availability. Material/Methods: A C++ simulation framework was developed based on the work of Frimodig [4]. The framework simulates fraction appointments scheduling over a defined period. Anonymized input data for ten treatments units were obtained from Iridium Netwerk (Antwerp, Belgium). Each simulated day, new patient registrations are generated based on the data and automatically scheduled using the framework to optimize average waiting times, appointment consistency, patient preferences, and overall treatment duration. A priority is assigned to each patient depending on urgency and treatment intent, with priority A being the most urgent. This study compares two scheduling scenarios: a baseline simulation based on 2020 Iridium Netwerk data, and an identical simulation in which all prostate (VMAT) treatments are hypofractionated to five fractions. This modification affects 440 out of 4,873 patients (priority C). Results: The hypofractionation scenario reduced the average waiting time for all patients and across all priorities. The mean waiting time decreased from 0.93 ± 1.81 days to 0.80 ± 1.43 days (a 14.6% improvement), with the largest reduction observed for Priority A patients (from 1.55 ± 2.09 to 1.27 ± 1.51 days, p-value=3e-6, two-sided Wilcoxon test). The histograms in Figure 1 illustrate this effect. Priority B and C distributions largely overlap ( Δ = –0.006 and –0.065 days, respectively). In total, 90,060 minutes of treatment time were saved across all machines.
Conclusion: This study shows that the impact of hypofractionation can be analyzed quantitatively using automated scheduling scenario exploration. This evaluation demonstrated improvements in patient throughput and reductions in waiting time variability, particularly for high-priority patients even though the modified fractionation applied to lower-priority cases. However, it should be noted that this analysis focused on a single protocol and a hypothetical scenario of hypofractionation, which may be considered unrealistic since not all patients are suitable for SBRT treatment. Nevertheless, impact analysis of clinical decisions is usually a useful tool to assist departments with clinical adoption. The impact of decisions on patient schedules and the ability to optimize clinical resources is a non-negligible advantage. References: [1] Palma G, Monti S, Conson M, Pacelli R, Cella L. Normal tissue complication probability (NTCP) models for modern radiation therapy. Semin Oncol. 2019 Jun;46(3):210-218. doi: 10.1053/j.seminoncol.2019.07.006. Epub 2019 Aug 13. PMID: 31506196. [2] Aitken K, et al. When less is more: The rising tide of hypofractionation. Clin Oncol (R Coll Radiol). 2022 May;34(5):277-279. doi: 10.1016/j.clon.2022.02.007. [3] Nahum AE. The radiobiology of hypofractionation. Clin Oncol (R Coll Radiol). 2015 May;27(5):260-269. doi: 10.1016/j.clon.2015.02.001. [4] Frimodig S, Mercier C, De Kerf G. Automated radiation therapy patient scheduling: A case study at a Belgian hospital. arXiv preprint arXiv:2303.12494. 2023. Keywords: Scheduling, radiotherapy, hypofractionation
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