Abstract
BackgroundPlanning 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. MethodsTwenty-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. ResultsAutoMBM 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). ConclusionsBy 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.
Highlights
Stereotactic radiosurgery (SRS) was offered only to treat limited number of lesions [1]
By plan crowdsourcing prior international plan challenge, autoplanning using the Muliple Brain Mets (AutoMBM) produces high and consistent plan quality independent of the planning experience and the institution that is crucial to addressing the technical bottleneck of stereotactic radiosurgery (SRS) to intracranial metastases
In order to perform crowd-knowledge-based planning benchmark of AutoMBM against other non-Automated planning (AP) treatment planning systems (TPS), the same planning protocol, as defined in the original TransTasmania Radiation Oncology Group (TROG) planning competition, was adhered to by this study (Supplementary Table S1) except that all participants were demanded to achieve 20 volume receiving x Gy or more (Gy) to cover 99% of every gross tumor volumes (GTV) as a hard constraint
Summary
Stereotactic radiosurgery (SRS) was offered only to treat limited number of lesions [1]. 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 interna tional 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 quality of AutoMBM was correlated with the planner’ experience and compared between academic and non-academic centers. AutoMBM’s plan quality did not differ between academic and non-academic centers and uncorrelated with planning experience (all p > 0.05)
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