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.
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.
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 \(100\%\) of the prescribed dose for the generated plans was on average \(98.98\%\) (\(99.36\%\) for manual plans) with an average planning time of \(3.04\pm 1.1\) min (\(20\pm 10\) min for manual plans). Further qualitative analysis was conducted by an expert planner who accepted \(90\%\) of the plans with some changes (\(60\%\) requiring minor changes & \(30\%\) requiring major changes).
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.
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This work was supported by the Canadian Institutes of Health Research (CIHR) (Grant Nos. CIHR MOP-1422439, CIHR PJT 152965).
Conflict of interest
The authors declare that they have no conflict of interest.
Availability of data and material
Implementation of our 3D cGANs technique is available in the following link: https://github.com/tajwarabraraleef/3Dpix2pix-for-prostate-brachytherapy.
Institutional ethics approval was obtained for the use of clinical data in this study.
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Aleef, T.A., Spadinger, I.T., Peacock, M.D. et al. Centre-specific autonomous treatment plans for prostate brachytherapy using cGANs. Int J CARS 16, 1161–1170 (2021). https://doi.org/10.1007/s11548-021-02405-1
- Low-dose-rate brachytherapy
- Prostate cancer
- Treatment planning
- Generative adversarial networks
- Simulated annealing