TH‐C‐AUD‐08: Comparison of Tomotherapy Dose Distributions for 6MV X‐Rays and Different Cobalt‐60 Source Designs Using Monte Carlo Methods
Bibliographic record
Abstract
Purpose: To investigate intensity modulated dose distributions for Co‐60 based tomotherapy with cylindrical and rectangular shaped source geometries for a typical head and neck case using Monte Carlo (MC) methods. Method and Materials: EGSnrc/BEAMnrc MC code has been used to model three Co‐60 tomotherapy units and a 6 MV (Varian 2100EX) unit. Two of the Co‐60 units, consisting of modified collimator systems with customized binary multi‐leaf collimator (BMLC) were modeled for 3 and 5 cm long rectangular sources and SADs of 70 and 80 cm, respectively. The other units were a conventional Co‐60 unit (T780c) with 2 cm diameter cylindrical source (MDS Nordion, Canada) and a 6 MV Linac with an addon MIMiC BMLC (NOMOS, USA). All the Co‐60 sources had identical active volumes. EGSnrc/DOSXYZnrc MC code was used to calculate the intensity modulated beamlets and fan beam dose profiles in a water phantom. The intensity modulated energy fluence profiles from a 6 MV Linac and two Co‐60 units using the same beam segments will be compared. Tomotherapy treatment plans for a typical H&N case were calculated. All plans were optimized for PTV, two neck nodes and the spinal cord using the same optimization parameters. The treatment planning was provided by an in‐house inverse planning system based on a multi‐objective gradient‐search approach using MC calculated dose data. Results: Comparisons of optimized dose distributions, dose difference maps and dose area histogram will be presented. Despite significant differences in fluence profiles for Co‐60 and 6 MV beams, optimized dose distribution for all plans met the dose‐volume and tolerance criteria. However, the integral doses are potentially higher for Co‐60 plans particularly with longer source size and shorter SAD. Conclusions: Co‐60 based tomotherapy with appropriate source and BMLC design is dosimetrically viable. Research supported (in‐kind) by MDS Nordion, Canada.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".