Poster - Thurs Eve-15: Comparison of Cobalt-60 and 6 MV linac based tomotherapy: A prostate case study
Bibliographic record
Abstract
Previous work reported by us has shown the potential for Cobalt-60 (Co-60) tomotherapy for sites with small separations such as in head and neck site. In this work we extend our investigations by comparing tomotherapy plans for the treatment of a typical prostate cancer obtained for 6 MV and Co-60 beams. Beam collimation was provided by the MIMiC® (NOMOS Corporation, Sewickly, PA) multi-leaf collimator (MLC). Both plans used 21 beam angles, each utilizing the central 10 leaf-pairs of the MLC for intensity modulation. An in-house inverse treatment planning program, based on the active-set conjugate gradient method, was used for dose-volume optimization. BEAMnrc and DOSXYZnrc Monte Carlo simulated beam and dose data, including inhomogeneity corrections, were used to calculate the optimized tomotherapy dose distributions. Prostate, rectum, and external body contours were outlined and dose-volume optimization objectives were set to deliver a minimum of 95% and a maximum of 105% of the 76 Gy dose prescription to the prostate and limiting only 20% of the rectum volume to receive ⩾ 70 Gy. A quantitative analysis of the dose distributions and dose-area histograms show that both Co-60 and 6 MV plans achieve the initial objectives for target (prostate) and organ at risk (rectum). Although the dose to the body and rectum for Co-60 is slightly higher than that for 6 MV, it satisfies the plan objectives based on the clinical dose tolerance. Our results demonstrate that Co-60 based tomotherapy can provide clinically competitive dose distributions for the treatment of prostate cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".