An offline adaptive planning method based on delivered accumulated dose for brachytherapy in cervical cancer
Year: 2025
Type: article
Abstract: Background and purpose In current clinical practice, independent treatment plan optimization for each fraction of brachytherapy might not be able to fully leverage the dosimetric advantage of the cervical cancer radiotherapy combining external beam radiotherapy (EBRT) and brachytherapy (BT). This study proposed an offline adaptive planning method based on accumulated dose for BT, aiming to improve the total dose distribution of the combined radiotherapy. Methods and materials This study retrospectively reviewed nine cervical cancer patients treated with EBRT followed by high-dose-rate BT. For each BT fraction, we used a multi-metric deformable image registration method to accumulate the dose distributions of previously delivered EBRT and BT. The accumulated dose distribution was then imported into a customized commercial BT treatment planning system as a background in the adaptive dose optimization. Main dosimetric parameters of the target and organs at risk (OARs) were compared between the adaptive BT (ABT) and conventional BT (CBT) planning methods. Results For approximately 70 % of the BT fractions, the ABT plans have lower D2cc to the bladder or rectum compared with the CBT plans. In terms of total dose evaluation, the ABT planning method resulted in a decrease in mean values of D2cc, V60 and V50 for the bladder (–1.9 ± 2.0 Gy EDQ2 , –1.2 ± 1.2 %, and –0.9 ± 1.1 %) and rectum (–2.1 ± 1.8 Gy EQD2 , –1.2 ± 1.2 %, and –1.4 ± 1.3 %). Conclusion The offline adaptive planning method could help decrease the doses to OARs and improve the total dose distribution of combined radiotherapy, showing promising prospects for clinical use.
Authors: Qi Fu, Yingjie Xu, Xi Yang, Jusheng An, Zhaohan Li, Manni Huang, Jianrong Dai
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,430
Award: G5917427175
OpenAlex ID: W4409534657
