Rapid Treatment Planning for Low-dose-rate Prostate Brachytherapy with TP-GAN

Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to produce arrangement of implantable radioactive seeds that deliver a minimum prescribed dose to the prostate whilst minimizing toxicity to healthy tissues. There can be multiple seed arrangements that satisfy this dosimetric criterion, not all deemed 'acceptable' for implant from a physician's perspective. This leads to plans that are subjective where quality of treatment depends on the expertise of the planner. We propose a method that learns to generate consistent treatment plans from a large pool of successful clinical data (961 patients). Our model is based on conditional generative adversarial networks that use a novel loss function for penalizing the model on spatial constraints of the seeds. An optional optimizer based on a simulated annealing (SA) algorithm can be used to further fine-tune the plans if necessary (determined by the treating physician). Performance analysis was conducted on 150 test cases demonstrating comparable results to that of the manual plans. On average, the clinical target volume covered by 100% of the prescribed dose was 98.9% for our method compared to 99.4% for manual plans. Moreover, using our model, the planning time was significantly reduced to an average of 3 s/plan (2.5 min/plan with the optional SA). Compared to this, manual planning at our centre takes around 20 min/plan.

Automated summary

The study presents a method using conditional generative adversarial networks to generate consistent treatment plans for low-dose-rate prostate brachytherapy. By learning from a large pool of clinical data, the proposed method achieved comparable results to manual plans with significantly reduced planning time.