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

Abstract

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.

Implementation of a Knowledge-Based Treatment Planning Model for Cardiac-Sparing Lung Radiation Therapy

“…The dosimetric improvements for both RP models also came at the cost of an increase in monitor units MU, 489±78 original vs 566±79 for the cardiac-sparing RP (p<0.001) and 569±76 for the clinical RP (p<0.001). This finding is consistent with that of Tahmbe et al who found that for their lung KBP model, re-optimized plans statistically significantly increased plan complexity in both MU and MU/degree 20 . Tahmbe et al further found that this increased complexity did not impact plan deliverability.…”

“…KBP has been widely implemented across many different disease sites and allows for a means of partially automating the treatment planning process and reducing variability in plan quality 19 . In the setting of lung irradiation, KBP studies have shown improvements in V5, V20, and mean lung dose 20 . Other KBP models have been used to incorporate more information to the treatment planning process, such as training a model with functional lung volumes to allow for more patient-specific optimization 21 .…”

Error Detectability of Isodose Volumes as ROIs in Prostate Intensity-modulated RT QA

“…In areas where the target and OAR are close to each other, dose distributions with steep dose gradients are often necessary, and the motion of the treatment device tends to be more complicated (20). In recent years, planning devices that can perform treatment planning for VMAT using machine learning have become commercially available, but treatment plans created by machine learning are reported to be more complex in terms of MLC movements than those created manually (21,22). As treatment plans become more complex, MLC positioning accuracy increases in importance (23), and patient-specific verification is more important for the safe delivery of radiation therapy.…”

Design and validation of an automated radiation therapy treatment planning approach for locally advanced lung cancer

“…5 This is typically achieved through standardization of structures of interest, optimization techniques, and priority values. Specific approaches include, but are not limited to, knowledgebased planning (KBP), [6][7][8] multi-criteria optimization, [9][10][11] and template based planning. 12,13 To this end, the Ethos Adaptive RT platform (Varian Medical Systems, Palo Alto, CA) has been designed with an automated treatment planning system (TPS) that generates plans from userprovided templates.…”