Dosimetric evaluation of manually and inversely optimized treatment planning for high dose rate brachytherapy of cervical cancer

Abstract

Background. To compare five inverse treatment planning methods with the conventional manually optimized planning approach for brachytherapy of cervical cancer with respect to dosimetric parameters.

Material and methods. Eighteen cervical cancer patients treated with magnetic resonance imaging (MRI)-guided high dose rate (HDR) brachytherapy were included in this study. Six plans were created for each of the 4 HDR brachytherapy fractions for each patient: 1 manually optimized and 5 inversely planned. Three of these were based on inverse planning simulated annealing (IPSA) with and without extra constraints on maximum doses of the target volume, and different constraints on doses to the organs at risk (OARs). In addition there were two plans based on dose to target surface points. The resulting dose-volume histograms were analyzed and compared from the dosimetric point of view by quantifying specific dosimetric parameters, such as clinical target volume (CTV) D₉₀, CTV D₁₀₀, conformal index (COIN), and D_2cm3 for rectum, bladder and the sigmoid colon.

Results. Manual optimization led to a mean target coverage of 78.3% compared to 87.5%, 91.7% and 82.5% with the three IPSA approaches (p < 0.001). Similar COIN values for manual and inverse optimization were found. The manual optimization led to better results with respect to the dose to the OARs expressed as D_2cm3. Overall, the best results were obtained with manual optimization and IPSA plans with volumetric constraints including maximum doses to the target volume.

Conclusions. Dosimetric evaluation of manual and inverse optimization approaches is indicating the potential of IPSA for brachytherapy of cervical cancer. IPSA with constraints of maximum doses to the target volume is closer related to manual optimization than plans with constraints only to minimum dose to the target volume and maximum doses to OARs. IPSA plans with proper constraints performed better than those based on dose to target surface points and manually optimized plans.

Acknowledgements

Financial support from the Cancer Research Funds of Radiumhemmet is gratefully acknowledged.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.