Advanced Collapsed cone Engine dose calculations in tissue media for <scp>COMS</scp> eye plaques loaded with I‐125 seeds
Bibliographic record
Abstract
Purpose To investigate the dose calculation accuracy of the Advanced Collapsed cone Engine (ACE) algorithm for ocular brachytherapy using a COMS plaque loaded with I‐125 seeds for two heterogeneous patient tissue scenarios. Methods The Oncura model 6711 I‐125 seed and 16 mm COMS plaque were added to a research version (v4.6) of the Oncentra® Brachy (OcB) treatment planning system (TPS) for dose calculations using ACE. Treatment plans were created for two heterogeneous cases: (a) a voxelized eye phantom comprising realistic eye materials and densities and (b) a patient CT dataset with variable densities throughout the dataset. ACE dose calculations were performed using a high accuracy mode, high‐resolution calculation grid matching the imported CT datasets (0.5 × 0.5 × 0.5 mm3), and a user‐defined CT calibration curve. The accuracy of ACE was evaluated by replicating the plan geometries and comparing to Monte Carlo (MC) calculated doses obtained using MCNP6. The effects of the heterogeneous patient tissues on the dose distributions were also evaluated by performing the ACE and MCNP6 calculations for the same scenarios but setting all tissues and air to water. Results Average local percent dose differences between ACE and MC within contoured structures and at points of interest for both scenarios ranged from 1.2% to 20.9%, and along the plaque central axis (CAX) from 0.7% to 7.8%. The largest differences occurred in the plaque penumbra (up to 17%), and at contoured structure interfaces (up to 20%). Other regions in the eye agreed more closely, within the uncertainties of ACE dose calculations (~5%). Compared to that, dose differences between water‐based and fully heterogeneous tissue simulations were up to 27%. Conclusions Overall, ACE dosimetry agreed well with MC in the tumor volume and along the plaque CAX for the two heterogeneous tissue scenarios, indicating that ACE could potentially be used for clinical ocular brachytherapy dosimetry. In general, ACE data matched the fully heterogeneous MC data more closely than water‐based data, even in regions where the ACE accuracy was relatively low. However, depending on the plaque position, doses to critical structures near the plaque penumbra or at tissue interfaces were less accurate, indicating that improvements may be necessary. More extensive knowledge of eye tissue compositions is still required.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".