SU‐HH‐AUD C‐05: Low Dose Rate Prostate Brachytherapy: A Tomosynthesis‐Based Intra‐Operative Post‐Implant Dose Evaluation
Bibliographic record
Abstract
Purpose: To develop an intra‐operative dose assessment procedure that can be performed after an I‐125 prostate seed implantation, while the patient is still under anaesthesia. To accomplish this, we reconstruct the 3D position of each seed and co‐register it with the prostate contour. Method and materials: Our seed detection method involves a tomosynthesis‐based filtered reconstruction of the volume of interest. For 24 patients, the required cone‐beam images were obtained from 7 projections acquired over an angle of 60° with an isocentric imaging system adjacent to the treatment table. A graphical user interface (GUI) has been developed to allow visualization of the final seed positions and to interactively introduce corrections in the seeds positioning, if needed. The co‐registration between the tomosynthesis‐based seed positions and the TRUS‐based prostate contour is performed by applying the same rigid transformation as the one derived from the best match between the planned and the reconstructed seed positions. Dose analysis is then performed based on the co‐registered images. Results: In a patient study with an average of 56 seeds per implant, the automatic tomosynthesis‐based reconstruction yields a detection rate of 96% of the seeds and less than 1 false‐positive seed per implant. The GUI allows the user to achieve a 100% detection rate in less than 5 minutes. The seed localization error obtained with a phantom study is (0.4±0.4) mm. This leads to small dosimetric relative errors on D90 and V100 of respectively 1.5% and 0.3%. Patient dose analyses have shown a significant reduction in the dosimetric parameters between the planned and the post‐operative dosimetry. The relative difference between planned and intra‐operative D90 and V100 are respectively (11±8)% and (4±3)%. Conclusion: Our reconstruction method has the potential to provide accurate intra‐operative prostate dosimetry, all in less than 10 minutes extra time added to the whole implantation procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".