US 11,735,309 B2
Method and system for automated quality assurance in radiation therapy
Thomas G. Purdie, Oakville (CA); Christopher James McIntosh, Toronto (CA); and Igor Svistoun, North York (CA)
Assigned to UNIVERSITY HEALTH NETWORK, Toronto (CA)
Filed by UNIVERSITY HEALTH NETWORK, Toronto (CA)
Filed on Mar. 21, 2018, as Appl. No. 15/927,414.
Application 15/927,414 is a division of application No. 14/898,060, granted, now 10,475,537, previously published as PCT/CA2014/050551, filed on Jun. 12, 2014.
Claims priority of provisional application 61/834,145, filed on Jun. 12, 2013.
Prior Publication US 2018/0211725 A1, Jul. 26, 2018
Int. Cl. G16H 20/40 (2018.01); A61N 5/10 (2006.01); G16H 40/20 (2018.01); G16H 20/30 (2018.01); G16H 20/10 (2018.01); G16H 30/40 (2018.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01)
CPC G16H 20/40 (2018.01) [A61N 5/103 (2013.01); G16H 10/60 (2018.01); G16H 20/10 (2018.01); G16H 20/30 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); A61N 5/1038 (2013.01); A61N 5/1048 (2013.01); A61N 2005/1041 (2013.01)] 36 Claims
OG exemplary drawing
 
1. A method for evaluating an aspect of a proposed treatment plan for radiation therapy the method comprising:
obtaining the aspect of a proposed radiation therapy treatment plan defining radiation therapy treatment for at least one treatment site, and a set of patient data for a patient using a processor that communicates with one or more memories storing the proposed treatment plan and the set of patient data, the set of patient data comprising at least one set of image data for the at least one treatment site or data derived from the at least one set of image data;
generating a quality assessment output of a calculated quality estimate for the aspect of the proposed radiation therapy treatment plan using the processor to access the proposed radiation therapy treatment plan and the set of patient data stored in the one or more memories and extract one or more plan features from the aspect of the proposed treatment plan and one or more patient features from the set of patient data to evaluate the aspect of the proposed treatment plan according to a quality assurance model of one or more machine-learned rules for automated quality assessment stored in the one or more memories, the machine-learned rules defining expected relationships between the one or more plan features, and the one or more patient features derived from the set of patient data;
generating the quality assurance model of the one or more machine-learned rules for automated quality assessment by one or more of methods selected from the group of: artificial neural networks, tree-based models, support vector machines, K-means, naïve Bayes, deep learning models, and non-linear, multivariate classification or regression models; and
wherein the quality assurance model of the one or more machine-learned rules for automated quality assessment stored in the one or more memories were developed or refined by machine learning trained on features extracted from a plurality of radiation therapy treatment plans.