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Centre-specific autonomous treatment plans for prostate brachytherapy using cGANs
Tajwar Abrar Aleef1, Ingrid T Spadinger2, Michael D Peacock3
1School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada. tajwaraleef@ece.ubc.ca.
Purpose:
In low-dose-rate prostate brachytherapy (LDR-PB), treatment planning is the process of determining the arrangement of implantable radioactive sources that radiates the prostate while sparing healthy surrounding tissues. Currently, these plans are prepared manually by experts incorporating the centre's planning style and guidelines. In this article, we develop a novel framework that can learn a centre's planning strategy and automatically reproduce rapid clinically acceptable plans.
Methods:
The proposed framework is based on conditional generative adversarial networks that learn our centre's planning style using a pool of 931 historical LDR-PB planning data. Two additional losses that help constrain prohibited needle patterns and produce similar-looking plans are also proposed. Once trained, this model generates an initial distribution of needles which is passed to a planner. The planner then initializes the sources based on the predicted needles and uses a simulated annealing algorithm to optimize their locations further.
Results:
Quantitative analysis was carried out on 170 cases which showed the generated plans having similar dosimetry to that of the manual plans but with significantly lower planning durations. Indeed, on the test cases, the clinical target volumes achieving [Formula: see text] of the prescribed dose for the generated plans was on average [Formula: see text] ([Formula: see text] for manual plans) with an average planning time of [Formula: see text] min ([Formula: see text] min for manual plans). Further qualitative analysis was conducted by an expert planner who accepted [Formula: see text] of the plans with some changes ([Formula: see text] requiring minor changes & [Formula: see text] requiring major changes).
Conclusion:
The proposed framework demonstrated the ability to rapidly generate quality treatment plans that not only fulfil the dosimetric requirements but also takes into account the centre's planning style. Adoption of such a framework would save significant amount of time and resources spent on every patient; boosting the overall operational efficiency of this treatment.
