CVMED-PHMar 18, 2021

Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN

arXiv:2103.09996v19 citations
Originality Incremental advance
AI Analysis

This addresses the problem of inconsistent and time-consuming treatment planning for prostate cancer patients, though it is incremental as it builds on existing GAN methods with a novel loss function.

The paper tackled the variability and subjectivity in treatment planning for low-dose-rate prostate brachytherapy by training a model on retrospective data to generate consistent plans, achieving comparable dosimetric results (98.9% vs. 99.4% coverage) and reducing planning time to 2.5 minutes with optimization or under 3 seconds without, compared to 20 minutes manually.

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 to the physician's/centre's preference, planning style, and expertise. We propose a method that aims to reduce this variability by training a model to learn from a large pool of successful retrospective LDR-PB data (961 patients) and create consistent plans that mimic the high-quality manual plans. 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 prehistorical plans. On average, the clinical target volume covering 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 2.5 mins/plan with SA, and less than 3 seconds without SA. Compared to this, manual planning at our centre takes around 20 mins/plan.

Code Implementations1 repo
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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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