SU-GG-T-131: A Linear Metric of Knowledge-Based IMRT Treatment Plan Quality for the Prostate

2010 ◽  
Vol 37 (6Part17) ◽  
pp. 3214-3214
Author(s):  
M S Freeman ◽  
V Chanyavanich ◽  
S K Das ◽  
J Y Lo
2018 ◽  
Vol 43 (4) ◽  
pp. 313-318 ◽  
Author(s):  
Jiayun Chen ◽  
Guishan Fu ◽  
Minghui Li ◽  
Yixin Song ◽  
Jianrong Dai ◽  
...  

2013 ◽  
Vol 40 (6Part23) ◽  
pp. 395-395
Author(s):  
M Fan ◽  
F DeBlois ◽  
K Sultanem ◽  
G Stroian

2015 ◽  
Author(s):  
◽  
Lindsey Appenzoller Olsen

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Knowledge-based planning (KBP) has become a prominent area of research in radiation oncology in the last five years. The development of KBP aims to address the lack of systematic quality control and plan quality variability in radiotherapy treatment planning by providing achievable, patient-specific optimization objectives derived from a model trained with a cohort of previously treated, site-specific plans. This dissertation intended to develop, evaluate, and implement a knowledge-based planning system to reduce variability and improve radiotherapy treatment plan quality. The project aimed to 1) develop and validate an algorithm to train mathematical models that predict dose-volume histograms for organs at risk in radiotherapy planning, 2) implement the algorithm into a software application in order to transfer the technology into clinical practice, and 3) evaluate the impact of the software system (algorithm + application) on reducing variability and improving radiotherapy treatment plan quality through knowledge transfer. The presented work demonstrates that a KBP model is beneficial to radiotherapy planning. The developed models adequately describe what is dosimetrically achievable for patient specific anatomy and have proven useful in outlier detection for quality control of radiotherapy planning. The KBP paradigm has also demonstrated ability to improve treatment plan quality through benchmarking and transfer of knowledge between institutions.


2016 ◽  
Vol 119 ◽  
pp. S83-S84
Author(s):  
J. Tol ◽  
M. Dahele ◽  
A. Delaney ◽  
B. Slotman ◽  
W. Verbakel

2014 ◽  
Vol 41 (6Part11) ◽  
pp. 230-230
Author(s):  
T Song ◽  
Z Tian ◽  
X Jia ◽  
L Zhou ◽  
S Jiang ◽  
...  

2009 ◽  
Vol 36 (12) ◽  
pp. 5497-5505 ◽  
Author(s):  
Binbin Wu ◽  
Francesco Ricchetti ◽  
Giuseppe Sanguineti ◽  
Misha Kazhdan ◽  
Patricio Simari ◽  
...  

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Nicholas Hardcastle ◽  
Olivia Cook ◽  
Xenia Ray ◽  
Alisha Moore ◽  
Kevin L. Moore ◽  
...  

Abstract Introduction Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer. Methods A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan. Results Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan. Conclusion KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.


Author(s):  
H. Geng ◽  
T.G. Giaddui ◽  
M. Radden ◽  
N. Lee ◽  
P. Xia ◽  
...  

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