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

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

In: PHYS MEDICA, Vol. 95, 2022, p. 73-82.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Chan, MKH, 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 2022, 'Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study', PHYS MEDICA, vol. 95, pp. 73-82. https://doi.org/10.1016/j.ejmp.2022.01.011

APA

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. (2022). Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study. PHYS MEDICA, 95, 73-82. https://doi.org/10.1016/j.ejmp.2022.01.011

Vancouver

Chan MKH, Gevaert T, Kadoya N, Dorr J, Leung R, Alheet S et al. Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study. PHYS MEDICA. 2022;95:73-82. https://doi.org/10.1016/j.ejmp.2022.01.011

Bibtex

@article{3c7db131d6174fdba772617bc6dd41f3,
title = "Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study",
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{\textquoteright}s tests for variances. Plan quality of AutoMBM was correlated with the planner{\textquoteright} 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). 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.",
keywords = "Plan crowdsourcing, Autoplanning, Stereotactic radiosurgery, Multiple brain metastases",
author = "M.K.H. Chan and T. Gevaert and N. Kadoya and J. Dorr and R. Leung and S. Alheet and A. Toutaoui and R. Farias and M. Wong and C. Skourou and M. Valenti and I. Farr{\'e} and C. Otero-Mart{\'i}nez and D. O'Doherty and J. Waldron and S. Hanvey and M. Grohmann and H. Liu",
year = "2022",
doi = "10.1016/j.ejmp.2022.01.011",
language = "Deutsch",
volume = "95",
pages = "73--82",
journal = "PHYS MEDICA",
issn = "1120-1797",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Multi-center planning study of radiosurgery for intracranial metastases through Automation (MC-PRIMA) by crowdsourcing prior web-based plan challenge study

AU - Chan, M.K.H.

AU - Gevaert, T.

AU - Kadoya, N.

AU - Dorr, J.

AU - Leung, R.

AU - Alheet, S.

AU - Toutaoui, A.

AU - Farias, R.

AU - Wong, M.

AU - Skourou, C.

AU - Valenti, M.

AU - Farré, I.

AU - Otero-Martínez, C.

AU - O'Doherty, D.

AU - Waldron, J.

AU - Hanvey, S.

AU - Grohmann, M.

AU - Liu, H.

PY - 2022

Y1 - 2022

N2 - 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). 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.

AB - 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). 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.

KW - Plan crowdsourcing

KW - Autoplanning

KW - Stereotactic radiosurgery

KW - Multiple brain metastases

U2 - 10.1016/j.ejmp.2022.01.011

DO - 10.1016/j.ejmp.2022.01.011

M3 - SCORING: Zeitschriftenaufsatz

VL - 95

SP - 73

EP - 82

JO - PHYS MEDICA

JF - PHYS MEDICA

SN - 1120-1797

ER -