Nilesh S. Tambe
Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses
Tambe, Nilesh S.; Pires, Isabel M.; Moore, Craig; Wieczorek, Andrew; Upadhyay, Sunil; Beavis, Andrew W.
Dr Isabel Monteiro dos Santos Pires I.Pires@hull.ac.uk
Senior Lecturer in Biomedical Science/ Head of the Hypoxia and Tumour Microenvironment group
Andrew Beavis A.Beavis@hull.ac.uk
Objectives. Volumetric modulated arc therapy (VMAT) allows for reduction of organs at risk (OAR) volumes receiving higher doses, but increases OAR volumes receiving lower radiation doses and can subsequently increasing associated toxicity. Therefore, reduction of this low-dose-bath is crucial. This study investigates personalizing the optimization of VMAT arc parameters (gantry start and stop angles) to decrease OAR doses. Materials and Methods. Twenty previously treated locally advanced non-small cell lung cancer (NSCLC) patients treated with half-arcs were randomly selected from our database. These plans were re-optimized with seven different arcs parameters; optimization objectives were kept constant for all plans. All resulting plans were reviewed by two clinicians and the optimal plan (lowest OAR doses and adequate target coverage) was selected. Furthermore, knowledge-based planning (KBP) model was developed using these plans as 'training data' to predict optimal arc parameters for individual patients based on their anatomy. Treatment plan complexity scores and deliverability measurements were performed for both optimal and original clinical plans. Results. The results show that different arc geometries resulted in different dose distributions to the OAR but target coverage was mostly similar. Different arc geometries were required for different patients to minimize OAR doses. Comparison of the personalized against the standard (2 half-arcs) plans showed a significant reduction in lung V5 (lung volume receiving 5 Gy), mean lung dose and mean heart doses. Reduction in lung V20 and heart V30 were statistically insignificant. Plan complexity and deliverability measurements show the test plans can be delivered as planned. Conclusions. Our study demonstrated that personalizing arc parameters based on an individual patient's anatomy significantly reduces both lung and heart doses. Dose reduction is expected to reduce toxicity and improve the quality of life for these patients.
Tambe, N. S., Pires, I. M., Moore, C., Wieczorek, A., Upadhyay, S., & Beavis, A. W. (2021). Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses. Biomedical Physics and Engineering Express, 7(6), https://doi.org/10.1088/2057-1976/ac2635
|Journal Article Type||Article|
|Acceptance Date||Sep 13, 2021|
|Online Publication Date||Sep 13, 2021|
|Publication Date||Nov 1, 2021|
|Deposit Date||Sep 20, 2021|
|Publicly Available Date||Sep 14, 2022|
|Journal||Biomedical Physics and Engineering Express|
|Peer Reviewed||Peer Reviewed|
This file is under embargo until Sep 14, 2022 due to copyright reasons.
Contact I.Pires@hull.ac.uk to request a copy for personal use.
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