Impact of edema and seed movement on the dosimetry of prostate seed implants
Bibliographic record
Abstract
This article summarizes current knowledge concerning the characterization of prostatic edema and intra-prostatic seed movement as these relate to dosimetry of permanent prostate implants, and reports the initial application to clinical data of a new edema model used in calculating pre- and post-implant dose distributions. Published edema magnitude and half-life parameters span a broad range depending on implant technique and measurement uncertainty, hence clinically applicable values should be determined locally. Observed intra-prostatic seed movements appear to be associated with particular aspects of implant technique and could be minimized by technique modification. Using an extended AAPM TG-43 formalism incorporating the new edema model, relative dose error RE associated with neglecting edema was calculated for three I-125 seed implants (18.9 cc, 37.6 cc, 60.2 cc) performed at our center. Pre- and post-plan RE average values and ranges in a 50 × 50 × 50 mm(3) calculation volume were similar at ~2% and ~0-3.5%, respectively, for all three implants; however, the spatial distribution of RE varied for different seed configurations. Post-plan values of D90 and V100 for prostate were reduced by ~2% and ~1%, respectively. In cases where RE is not clinically negligible as a consequence of large edema magnitude and / or use of Pd-103 seeds, the dose calculation method demonstrated here can be applied to account for edema explicitly and there by improve the accuracy of clinical dose estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".