Model-based dose calculations for<sup>125</sup>I lung brachytherapy
Bibliographic record
Abstract
PURPOSE: Model-baseddose calculations (MBDCs) are performed using patient computed tomography (CT) data for patients treated with intraoperative (125)I lung brachytherapy at the Mayo Clinic Rochester. Various metallic artifact correction and tissue assignment schemes are considered and their effects on dose distributions are studied. Dose distributions are compared to those calculated under TG-43 assumptions. METHODS: Dose distributions for six patients are calculated using phantoms derived from patient CT data and the EGSnrc user-code BrachyDose. (125)I (GE Healthcare/Oncura model 6711) seeds are fully modeled. Four metallic artifact correction schemes are applied to the CT data phantoms: (1) no correction, (2) a filtered back-projection on a modified virtual sinogram, (3) the reassignment of CT numbers above a threshold in the vicinity of the seeds, and (4) a combination of (2) and (3). Tissue assignment is based on voxel CT number and mass density is assigned using a CT number to mass density calibration. Three tissue assignment schemes with varying levels of detail (20, 11, and 5 tissues) are applied to metallic artifact corrected phantoms. Simulations are also performed under TG-43 assumptions, i.e., seeds in homogeneous water with no interseed attenuation. RESULTS: Significant dose differences (up to 40% for D(90)) are observed between uncorrected and metallic artifact corrected phantoms. For phantoms created with metallic artifact correction schemes (3) and (4), dose volume metrics are generally in good agreement (less than 2% differences for all patients) although there are significant local dose differences. The application of the three tissue assignment schemes results in differences of up to 8% for D(90); these differences vary between patients. Significant dose differences are seen between fully modeled and TG-43 calculations with TG-43 underestimating the dose (up to 36% in D(90)) for larger volumes containing higher proportions of healthy lung tissue. CONCLUSIONS: Metallic artifact correction is necessary for accurate application of MBDCs for lung brachytherapy; simpler threshold replacement methods may be sufficient for early adopters concerned with clinical dose metrics. Rigorous determination of voxel tissue parameters and tissue assignment is required for accurate dose calculations as different tissue assignment schemes can result in significantly different dose distributions. Significant differences are seen between MBDCs and TG-43 dose distributions with TG-43 underestimating dose in volumes containing healthy lung tissue.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".