Towards U-Net-based intraoperative 2D dose prediction in high dose rate prostate brachytherapy
Bibliographic record
Abstract
BACKGROUND: Poor needle placement in prostate high-dose-rate brachytherapy (HDR-BT) results in sub-optimal dosimetry and mentally predicting these effects during HDR-BT is difficult, creating a barrier to widespread availability of high-quality prostate HDR-BT. PURPOSE: To provide earlier feedback on needle implantation quality, we trained machine learning models to predict 2D dosimetry for prostate HDR-BT on axial TRUS images. METHODS AND MATERIALS: Clinical treatment plans from 248 prostate HDR-BT patients were retrospectively collected and randomly split 80/20 for training/testing. Fifteen U-Net models were implemented to predict the 90%, 100%, 120%, 150%, and 200% isodose levels in the prostate base, midgland, and apex. Predicted isodose lines were compared to delivered dose using Dice similarity coefficient (DSC), precision, recall, average symmetric surface distance, area percent difference, and 95th percentile Hausdorff distance. To benchmark performance, 10 cases were retrospectively replanned and compared against the clinical plans using the same metrics. RESULTS: Models predicting 90% and 100% isodose lines at midgland performed best, with median DSC of 0.97 and 0.96, respectively. Performance declined as isodose level increased, with median DSC of 0.90, 0.79, and 0.65 in the 120%, 150%, and 200% models. In the base, median DSC was 0.94 for 90% and decreased to 0.64 for 200%. In the apex, median DSC was 0.93 for 90% and decreased to 0.63 for 200%. Median prediction time was 25 ms. CONCLUSION: U-Net models accurately predicted HDR-BT isodose lines on 2D TRUS images sufficiently quickly for real-time use. Incorporating auto-segmentation algorithms will allow intra-operative feedback on needle implantation quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".