Sci‐Sat AM(2): Brachy‐07: Tomosynthesis‐based seed reconstruction in LDR prostate brachytherapy: A clinical study
Bibliographic record
Abstract
To develop a tomosynthesis-based dose assessment procedure that can be performed after an I-125 prostate seed implantation, while the patient is still under anaesthesia on the treatment table. Our seed detection procedure involves the reconstruction of a volume of interest based on the backprojection of 7 seed-only binary images acquired over an angle of 60° with an isocentric imaging system. A binary seed-only volume is generated by a simple thresholding of the volume of interest. Seeds positions are extracted from this volume with a 3D connected component analysis and a statistical classifier that determines the number of seeds in each cluster of connected voxels. A graphical user interface (GUI) allows to visualize the result and to introduce corrections, if needed. A phantom and a clinical study (24 patients) were carried out to validate the technique. A phantom study demonstrated a very good localization accuracy of (0.4+/-0.4) mm when compared to CT-based reconstruction. This leads to dosimetric error on D90 and V100 of respectively 0.5% and 0.1%. 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 1.5% of false-positives. With the help of the GUI, the user can achieve a 100% detection rate in an average of 3 minutes. This technique would allow to identify possible underdosage and to correct it by potentially reimplanting additional seeds. A more uniform dose coverage could then be achieved in LDR prostate brachytherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".