Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients

Author(s):  
Nilesh S Tambe ◽  
Isabel M Pires ◽  
Craig S Moore ◽  
Andrzej Wieczorek ◽  
Sunil Upadhyay ◽  
...  
2016 ◽  
Vol 120 (2) ◽  
pp. 349-355 ◽  
Author(s):  
Sean L. Berry ◽  
Rongtao Ma ◽  
Amanda Boczkowski ◽  
Andrew Jackson ◽  
Pengpeng Zhang ◽  
...  

2019 ◽  
Vol 9 (2) ◽  
pp. e218-e227 ◽  
Author(s):  
James A. Kavanaugh ◽  
Sarah Holler ◽  
Todd A. DeWees ◽  
Clifford G. Robinson ◽  
Jeffrey D. Bradley ◽  
...  

2020 ◽  
Vol 152 ◽  
pp. S96-S97
Author(s):  
E. Adams ◽  
S. Currie ◽  
C. Thomas ◽  
A. Pediaditaki ◽  
S. Temple ◽  
...  

Author(s):  
Justin Visak ◽  
Aaron Webster ◽  
Mark E. Bernard ◽  
Mahesh Kudrimoti ◽  
Marcus E. Randall ◽  
...  

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 375-375 ◽  
Author(s):  
Hunter C Gits ◽  
Janell Dow ◽  
Martha Matuszak ◽  
Mary Uan-Sian Feng

375 Background: SBRT is emerging as a treatment option for patients with unresectable liver tumors. However, generating high quality liver SBRT plans is technically challenging and user-dependent. Aiming for high tumor doses and low normal tissue doses can demand lengthy planning times and result in plans of inconsistent quality. This study investigates knowledge-based planning (KBP) as a standardized method to ensure efficacy, safety, and efficiency for SBRT plans and allow for broader use of this therapy. Methods: SBRT treatment plans were manually optimized for 55 liver cancer cases by an expert liver dosimetrist in a commercial treatment planning system (Varian Eclipse v13.6). Each volumetric modulated arc therapy plan was approved by a physician using strict criteria for target coverage and normal tissue sparing. The plans were used to create a custom model in a KBP system (RapidPlan, Varian Eclipse v13.6) designed to improve both efficiency and standardization of quality. To validate the model, 15 new cases were optimized manually by an expert liver dosimetrist and then semi-automatically using the KBP model. The validation plans were compared based on target coverage, normal tissue sparing, and planning time. Results: Compared to manual plans created by an expert liver dosimetrist, KBP-generated plans showed similar target coverage and improved normal tissue sparing with similar planning times. Mean and minimum target doses were similar, as was D98, p > 0.2 for all. Normal tissue complication probability for liver damage was marginally lower with KBP, mean 3 vs. 1%, p = 0.07. Doses to adjacent organs including stomach, heart, and bowel were similar. Manual planning required a median and mean time of 15 and 20 minutes, respectively, range 8-55 min. KBP required similar times of 12 and 19 min, range 8-50 min. 9 of 15 KBP cases were automated, while 6 plans required dosimetrist improvement. Conclusions: Using KBP, high quality plans for liver SBRT can be created automatically or semi-automatically. These plans are comparable to those generated by an expert liver dosimetrist. KBP could be used to standardize treatments between institutions, particularly when experience with liver SBRT is limited.


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