Paper Proffered Program Therapy Physics

Up-to-Now Optimization for Online Adaptive Radiotherapy

Abstract
Purpose

To develop and evaluate an online adaptive radiotherapy (ART) VMAT optimization that incorporates accumulated dose up-to-now (UTN-Opt). This strategy is demonstrated in silico on a clinically motivated esophageal cancer case on a C-arm linac.

Methods

UTN-Opt was implemented within our in-house optimizer. Delivered fraction doses were accumulated on the daily synthetic CT. During ART plan optimization, the objective function was calculated on the sum of the accumulated dose and the currently optimized, yet-to-be-delivered dose. In the UTN-daily-ART workflow, plans were adapted daily using UTN-Opt. In the UTN-weekly-ART workflow, UTN-Opt was performed online weekly (fractions 5, 10, 15, 20, 25, 28), with the resulting plan used for the next fractions. A clinically motivated esophageal cancer case comprising 28 synthetic CTs with a prescription dose of 50.4 Gy over 28 fractions was used to compare: (1) conventional non-adaptive radiotherapy (cRT); (2) standard online ART (oART); (3) UTN-daily-ART; (4) UTN-weekly-ART. All plans were created using our in-house optimizer with the same objective function. For comparison, all fraction doses were accumulated on the last synthetic CT.

Results

Both UTN strategies improved target coverage compared to oART and cRT. CTV D95% increased by 2.0 Gy (UTN-daily-ART) and 1.2 Gy (UTN-weekly-ART) and mean heart dose decreased with UTN strategies by 0.5 Gy (UTN-daily-ART) and 1.3 Gy (UTN-weekly-ART) compared to oART. UTN strategies increased CTV D5% and dose to other OARs compared to oART and cRT. However, all OAR doses were clinically acceptable for all four strategies.

Conclusion

We successfully developed and demonstrated the feasibility of UTN-Opt in an online adaptive radiotherapy workflow for a first clinically motivated case. UTN-Opt substantially improved target coverage compared to cRT and oART while maintaining clinically acceptable OAR doses. Supported by SNSF grant 10001696 and by Varian, a Siemens Healthineers Company.

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