Background: Planning radiosurgery to multiple intracranial metastases is complex and shows large variability in dosimetric quality among planners and treatment planning systems (TPS). This project aimed to determine whether autoplanning using the Muliple Brain Mets (AutoMBM) software can improve plan quality and reduce inter-planner variability by crowdsourcing results from prior international planning study.
Methods: Twenty-four institutions autoplanned with AutoMBM on a five metastases case from a prior international planning competition from which population statistics (means and variances) of 23 dosimetric metrics and resulting composite plan score (maximum score = 150) of other TPS (Eclipse, Monaco, RayStation, iPlan, GammaPlan, MultiPlan) were crowdsourced. Plan results of AutoMBM and each of the other TPS were compared using two sample t-tests for means and Levene's tests for variances. Plan quality of AutoMBM was correlated with the planner' experience and compared between academic and non-academic centers.
Results: AutoMBM produced plans with comparable composite plan score to GammaPlan, MultiPlan, Eclipse and iPlan (127.6 vs. 131.7 vs. 127.3 vs. 127.3 and 126.7; all p > 0.05) and superior to Monaco and RayStation (118.3 and 108.6; both p < 0.05). Inter-planner variability of overall plan quality was lowest for AutoMBM among all TPS (all p < 0.05). AutoMBM's plan quality did not differ between academic and non-academic centers and uncorrelated with planning experience (all p > 0.05).
Conclusions: By plan crowdsourcing prior international plan challenge, AutoMBM produces high and consistent plan quality independent of the planning experience and the institution that is crucial to addressing the technical bottleneck of SRS to intracranial metastases.
Chan, M K H; Gevaert, T; Kadoya, N; Dorr, J; Leung, R; Alheet, S; Toutaoui, A; Farias, R; Wong, M; Skourou, C; Valenti, M; Farré, I; Otero-Martínez, C; O'Doherty, D; Waldron, J; Hanvey, S; Grohmann, M; and Lui, Haisong, "Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study" (2022). Department of Radiation Oncology Faculty Papers. Paper 162.
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