Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study. (March 2022)

• Record Type: Journal Article
• Authors: 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.; Liu, H.
• Is Part Of: Physica medica. Volume 95(2022)
• Page Start: 73
• Page End: 82
• Publication Date: 2022-03
• Subjects: AP autoplanning -- TPS treatment planning system -- SRS stereotactic radiosurgery -- DCA dynamic conform arc -- VMAT volumetric modulated arc radiotherapy -- MLC multi-leaf collimator -- PCI Paddict conformity index -- GI dose gradient index -- R50% spread of half isodose line -- Vx Gy volume receiving x Gy or more -- D x cm3 dose to x cm3 of the volume; Plan crowdsourcing -- Autoplanning -- Stereotactic radiosurgery -- Multiple brain metastases; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2022.01.011
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Retrospective benchmark of AP for SRS to multi-intracranial metastases on the MBM software through plan crowdsourcing. Significant improvement of plan dosimetry by MBM over other TPS for linac-based SRS was achieved via AP. Inter-planner variability of plan dosimetry could be reduced through AP regardless of linac treatment platforms. Elimination of dependence of plan quality on planning experience is possible through AP. AP shows promises in standardizing plan quality across academic and non-academic centers. Abstract: 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.