scholarly journals Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190535
Author(s):  
Nilesh S Tambe ◽  
Isabel M Pires ◽  
Craig Moore ◽  
Christopher Cawthorne ◽  
Andrew W Beavis
Keyword(s):  
Lung Volume ◽  
Clinical Implementation ◽  
Lung Dose ◽  
Treatment Delivery ◽  
Lung Cancer Patients ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Dose Metrics ◽  
Treatment Plans ◽  
Stage Lung Cancer

Objectives: Radiotherapy plan quality may vary considerably depending on planner’s experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. Methods: Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. Results: The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. Conclusion: Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. Advances in knowledge: In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

scholarly journals The Dosimetric and Temporal Effects of Respiratory-Gated, High-Dose-Rate Radiation Therapy in Patients With Lung Cancer

2018 ◽  
Vol 18 ◽  
pp. 153303381881607 ◽  
Author(s):  
Ouided Rouabhi ◽  
Brandie Gross ◽  
John Bayouth ◽  
Junyi Xia
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Dose Rate ◽  
Treatment Time ◽  
High Dose Rate ◽  
High Dose ◽  
Lung Dose ◽  
Treatment Delivery ◽  
Treatment Plans ◽  
Mean Lung Dose

Purpose: To evaluate the dosimetric and temporal effects of high-dose-rate respiratory-gated radiation therapy in patients with lung cancer. Methods: Treatment plans from 5 patients with lung cancer (3 nongated and 2 gated at 80EX-80IN) were retrospectively evaluated. Prescription dose for these patients varied from 8 to 18 Gy/fraction with 3 to 5 treatment fractions. Using the same treatment planning criteria, 4 new treatment plans, corresponding to 4 gating windows (20EX-20IN, 40EX-40IN, 60EX-60IN, and 80EX-80IN), were generated for each patient. Mean tumor dose, mean lung dose, and lung V20 were used to assess the dosimetric effects. A MATLAB algorithm was developed to compute treatment time. Results: Mean lung dose and lung V20 were on average reduced between −16.1% to −6.0% and −20.0% to −7.2%, respectively, for gated plans when compared to the corresponding nongated plans, and between −5.8% to −4.2% and −7.0% to −5.4%, respectively, for plans with smaller gating windows when compared to the corresponding plans gated at 80EX-80IN. Treatment delivery times of gated plans using high-dose rate were reduced on average between −19.7% (−0.10 min/100 MU) and −27.2% (−0.13 min/100 MU) for original nongated plans and −15.6% (−0.15 min/100 MU) and −20.3% (−0.19 min/100 MU) for original 80EX-80IN-gated plans. Conclusion: Respiratory-gated radiation therapy in patients with lung cancer can reduce lung dose while maintaining tumor dose. Because treatment delivery during gated therapy is discontinuous, total treatment time may be prolonged. However, this increase in treatment time can be offset by increasing the dose delivery rate. Estimation of treatment time may be helpful in selecting patients for respiratory gating and choosing appropriate gating windows.

scholarly journals PO-0674: SABR for lung tumors of 5cm or more: can knowledge-based planning detect high-risk treatment plans?

2017 ◽  
Vol 123 ◽  
pp. S352-S353
Author(s):  
S. Van 't Hof ◽  
M. Dahele ◽  
H. Tekatli ◽  
A. Delaney ◽  
J. Tol ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Tumors ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Treatment Plans ◽  
Risk Treatment

scholarly journals Clinical implementation of a knowledge based planning tool for prostate VMAT

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Richard Powis ◽  
Andrew Bird ◽  
Matthew Brennan ◽  
Susan Hinks ◽  
Hannah Newman ◽  
...  
Keyword(s):  
Planning Tool ◽  
Clinical Implementation ◽  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals Knowledge-based planning for stereotactic radiotherapy of peripheral early-stage lung cancer

2017 ◽  
Vol 56 (3) ◽  
pp. 490-495 ◽  
Author(s):  
Alexander R. Delaney ◽  
Max Dahele ◽  
Jim P. Tol ◽  
Ben J. Slotman ◽  
Wilko F. A. R. Verbakel
Keyword(s):  
Lung Cancer ◽  
Stereotactic Radiotherapy ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Stage Lung Cancer

scholarly journals Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Nicholas Hardcastle ◽  
Olivia Cook ◽  
Xenia Ray ◽  
Alisha Moore ◽  
Kevin L. Moore ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Real Time ◽  
Kidney Cancer ◽  
Treatment Plan ◽  
Plan Quality ◽  
Knowledge Based ◽  
Trade Offs ◽  
Stereotactic Ablative Body Radiotherapy ◽  
Knowledge Based Planning ◽  
Treatment Plans

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.

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