Automatic post‐implant needle reconstruction algorithm to characterize and improve implant robustness analyses
Bibliographic record
Abstract
Post-implant analysis in permanent implant brachytherapy is an important process that provides a feedback on treatment quality. Random seed movements, edema, and needle related factors contribute to deteriorate dose coverage. For a complete study of these movements, it is important to reconstruct the post-implant seeds clusters but, up to now, this task was only possible via a long and difficult manual process. To facilitate post-implant analysis a simulated annealing algorithm was developed to perform automatic reconstructions. This process is fast (30-60 s on a 1.3 GHz pentium) and has a high level of success, even with up to 5% of seed loss. Tests on 21 clinical cases show that the algorithm yields exactly the same results as manual reconstructions. A realistic simulation tool was used to generate 58 synthetic post-implant data, in which cases the exact configuration was known. Even if some errors were found, pertinent information was extracted. For medium seed density [corresponding to seeds of 0.6 mCi (0.762 U)], 97% of seeds are matched with their correct needle and 89% are matched with their correct planned position. This method provides pertinent information that can be used to understand inhomogenous dose coverage in specific prostate quadrants; to make realistic post-implant simulations or to identify seeds belonging to a needle loaded with different seed types or activity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".