scholarly journals Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials

2019 ◽  
Vol 9 (2) ◽  
pp. e218-e227 ◽  
Author(s):  
James A. Kavanaugh ◽  
Sarah Holler ◽  
Todd A. DeWees ◽  
Clifford G. Robinson ◽  
Jeffrey D. Bradley ◽  
...  
2021 ◽  
Vol 10 ◽  
Author(s):  
Jiang Hu ◽  
Boji Liu ◽  
Weihao Xie ◽  
Jinhan Zhu ◽  
Xiaoli Yu ◽  
...  

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.


2016 ◽  
Vol 120 (2) ◽  
pp. 349-355 ◽  
Author(s):  
Sean L. Berry ◽  
Rongtao Ma ◽  
Amanda Boczkowski ◽  
Andrew Jackson ◽  
Pengpeng Zhang ◽  
...  

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.


2020 ◽  
Vol 152 ◽  
pp. S94-S95
Author(s):  
K. Fjellanger ◽  
H.E.S. Pettersen ◽  
J.A. Hundvin ◽  
B. Nygaard ◽  
K. Revheim ◽  
...  

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