IMRT clinical implementation: Prostate and pelvic node irradiation using Helios and a 120‐leaf multileaf collimator
Bibliographic record
Abstract
Dynamic intensity modulated radiation therapy (IMRT) to treat prostate and pelvic nodes using the Varian 120-leaf Millennium multileaf collimator (MLC) has been implemented in our clinic. This paper describes the procedures that have been undertaken to achieve this, including some of the commissioning aspects of Helios, verification of the dynamic dose delivery, and quality assurance (QA) of the dose delivered to the patient. Commissioning of Helios included measurements of transmission through the 120-leaf MLC, which were found to be 1.7% for 6 mV and 1.8% for 10 MV. The rounded leaf edge effect, known as the dosimetric separation, was also determined using two independent methods. Values of 1.05 and 1.65 mm were obtained for 6 and 10 MV beams. Five test patients were planned for prostate and pelvic node irradiation to 70 and 50 Gy, respectively. Dose and fluence verification were carried out on specially designed phantoms and dose points in the prostate were measured to be within 2.0% (mean 0.9%, s.d. 0.6%) of the calculated dose and in the nodes within 3.0% (mean 1.6%, s.d. 1.1%). Following the results of this commissioning and implementation study, we have started to treat men with a target Volume including the prostate and pelvic nodes using Helios optimized dynamic IMRT delivery in a dose escalation protocol.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".