scholarly journals Improving efficiency in the radiation management of multiple brain metastases using a knowledge‐based planning solution for single‐isocentre volumetric modulated arc therapy (VMAT) technique

2021 ◽  
Author(s):  
James O’Toole ◽  
Maddison Picton ◽  
Mario Perez ◽  
Michael Back ◽  
Dasantha Jayamanne ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Volumetric Modulated Arc Therapy ◽  
Multiple Brain Metastases ◽  
Knowledge Based ◽  
Arc Therapy ◽  
Knowledge Based Planning ◽  
Radiation Management

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.

scholarly journals EP-1829 Clinical validation of knowledge-based planning for multiple brain metastases

2019 ◽  
Vol 133 ◽  
pp. S992-S993
Author(s):  
N. Kishi ◽  
M. Nakamura ◽  
H. Hirashima ◽  
N. Mukumoto ◽  
K. Takehana ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Clinical Validation ◽  
Multiple Brain Metastases ◽  
Knowledge Based ◽  
Knowledge Based Planning

A method for optimizing LINAC treatment geometry for volumetric modulated arc therapy of multiple brain metastases

2010 ◽  
Vol 37 (8) ◽  
pp. 4146-4154 ◽  
Author(s):  
Jun Kang ◽  
Eric C. Ford ◽  
Koren Smith ◽  
John Wong ◽  
Todd R. McNutt
Keyword(s):  
Brain Metastases ◽  
Volumetric Modulated Arc Therapy ◽  
Multiple Brain Metastases ◽  
Arc Therapy
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