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"Plan-library supported automated re-planning for online-adaptive IMPT of cervical cancer"

Thyrza Jagt, Sebastiaan Breedveld, Rens van Haveren, Remi Nout, Eleftheria Astreinidou, Marius Staring, Ben Heijmen and Mischa Hoogeman

Abstract

Purpose/objective: Intensity-modulated proton therapy (IMPT) is very sensitive to small daily density variations along the pencil beam paths and variations in target and OAR shapes. This makes IMPT for sites with large inter-fraction target deformations extra challenging, such as in the treatment of cervical cancer. Online replanning is an option to achieve adequate target dose in each fraction. This study evaluates a novel approach employing a pre-treatment established plan-library as prior information in automated online replanning for IMPT of cervical cancer.

Material and Methods: CT data of 5 cervical cancer patients was available, comprising of a full and empty-bladder CT and 3-4 repeat CTs. Prescribed dose for the primary tumor and pelvic ąpara-aortic lymph nodes was 45 Gy. Pre-treatment plan-libraries were created to provide prior spot distributions for replanning on the repeat CTs. One consisted of two treatment plans based on the full and empty-bladder CT +8 mm margin and the other of one treatment plan encompassing all target deformation observed in the full and empty-bladder CT +10 mm margin, i.e. a large ITV. In case of the 2-plan-library the daily bladder volume was used to select the prior plan for replanning.
The reoptimization method starts with a spot-position (Bragg peak) restoration from the selected prior plan by adjusting the energy of each pencil beam to the water equivalent path length in the repeat CT. To further compensate for deformations, new spots are added. The reference point method (RPM) is then used to optimize the spot weights. The RPM has been automatically tuned on benchmark plans of 4 CTs (i.e. optimized from scratch without time constraints) and results in a reoptimized Pareto optimal plan for the new anatomy, with similar trade-offs as in the benchmark plan. Replanning was performed for each repeat CT using tight margins of 5/2 mm (primary tumor/nodes), only meant to account for intra-fraction motion. The prior and reoptimized plans were evaluated on the repeat CTs using the 5/2 mm-PTVs and compared to benchmark plans on the repeat CTs.

Results: Evaluating the prior plans on the repeat CTs without replanning resulted in V95%<95% in most CTs, with values down to 50% (see Fig 1). For both plan-library approaches, reoptimization increased the number of repeat CTs with adequate coverage (PTV V95%=95% and V107%=2%) from 2/19 to 19/19 CTs. Fig 2 shows the differences between the reoptimized and benchmark plans on the repeat CTs using the ITV or 2-plan-library as prior. Median improvements are seen up to 4.5%-point for bladder V30Gy when using the 2-plan-library instead of the ITV plan, with outliers up to 13.8%-point. Reoptimization took 3.6 min on average.

Conclusion: With fully automated replanning, adequate target coverage was restored for all CTs, as well as decreased OAR doses. The use of a 2-plan-library yielded lower OAR doses than a single ITV prior plan. With an average time of 3.6 minutes, this method is an important step towards online-adaptive IMPT in cervical cancer.

 

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Copyright © 2019 by the authors. Published version © 2019 by . Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Jagt:2019,
author = {Jagt, Thyrza and Breedveld, Sebastiaan and van Haveren, Rens and Nout, Remi and Astreinidou, Eleftheria and Staring, Marius and Heijmen, Ben and Hoogeman, Mischa},
title = {Plan-library supported automated re-planning for online-adaptive IMPT of cervical cancer},
journal = {Radiotherapy and Oncology (ESTRO)},
volume = {133},
number = {Supplement 1},
pages = {S38 - S39},
month = {April},
year = {2019},
}

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