scholarly journals Use of a constrained hierarchical optimization dataset enhances knowledge-based planning as a quality assurance tool for prostate bed irradiation

2018 ◽  
Vol 45 (10) ◽  
pp. 4364-4369 ◽  
Author(s):  
Yen Hwa Lin ◽  
Linda X. Hong ◽  
Margie A. Hunt ◽  
Sean L. Berry
Keyword(s):  
Quality Assurance ◽  
Hierarchical Optimization ◽  
Knowledge Based ◽  
Prostate Bed ◽  
Knowledge Based Planning

scholarly journals Knowledge-Based Planning as a Real Time Review Quality Assurance Feedback Tool in the TROG 1501 SPARK Trial

2018 ◽  
Vol 102 (3) ◽  
pp. e535-e536
Author(s):  
O.M. Cook ◽  
A.J. Moore ◽  
R. Kaderka ◽  
K.L. Moore ◽  
J. Martin ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Real Time ◽  
Knowledge Based ◽  
Knowledge Based Planning

Knowledge Based Quality Assurance Tools

1990 ◽  
Author(s):  
Stephan S. Yau ◽  
Gwo-Long Huang ◽  
Jinshuan Lee ◽  
Yeou-Wei Wang
Keyword(s):  
Quality Assurance ◽  
Knowledge Based

Knowledge-Based Planning

1993 ◽  
Author(s):  
Drew McDermott ◽  
Gregory Hager
Keyword(s):  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset

2021 ◽  
Author(s):  
Aaron Babier ◽  
Binghao Zhang ◽  
Rafid Mahmood ◽  
Kevin L. Moore ◽  
Thomas G. Purdie ◽  
...  
Keyword(s):  
Open Access ◽  
Grand Challenge ◽  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

Sign in / Sign up

Export Citation Format

Share Document