Post-implant analysis in permanent breast seed implant: automated plan reconstruction using simulated annealing
Bibliographic record
Abstract
PURPOSE: Post-implant analysis in permanent breast seed implant (PBSI) brachytherapy is an important component of the quality assurance process that indicates dosimetric quality relevant to patient outcome, indicating salvage therapy if inadequate, as well as providing feedback to the brachytherapy team to improve future treatments. To measure geometric indices on implant quality, plan reconstruction must be performed to correlate each planned and post-implant seed location. In this work, a simulated-annealing-based algorithm is developed to perform this plan reconstruction automatically. MATERIAL AND METHODS: The plan reconstruction algorithm was developed in MATLAB, taking the patient pre-treatment and post-implant (Day 0) plan and associated contours as inputs. For 19 treated patients, a reconstruction was obtained that defined the correspondence between each planned and post-implant seed. The simulated-annealing algorithm was used to reconstruct each patient 10 times to assess the variability in convergence. Manual reconstructions performed by at least two independent observers to obtain consensus were defined as the ground truth; these were compared to the automatic reconstructions obtained by the algorithm. Metrics on seed placement accuracy and needle strand angulation were calculated for the patients. RESULTS: The algorithm performed reconstructions on 19 patients (1235 seeds) with ground-truth reconstructions, obtaining 97 ± 8% correct matches. This strong performance indicates the ability to incorporate this algorithm into the clinical quality assurance workflow. CONCLUSIONS: The plan reconstruction algorithm developed herein performed very well in a 19-patient cohort. This algorithm can be incorporated into the clinical process to assist in the assessment of center-specific seed placement accuracy and can be used to gather implant metrics in an automated, standardized fashion for future PBSI trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".