SU‐F‐T‐451: Doses to Organs‐At‐Risk in the Presence and Absence of a 1.5 T Magnetic Field for NSCLC Patients Undergoing SABR
Bibliographic record
Abstract
Purpose: To determine whether the electron return effect (ERE) has deleterious effects on lung SABR plans optimized in the presence of an orthogonal 1.5 T magnetic field. Methods: Data from five NSCLC‐SABR patients were used. The Dose was modeled with a 2.5 mm dose grid in the presence and absence of a magnetic field using the Monaco (Elekta) TPS with the Monte Carlo GPUMCD (v5.1) algorithm. For each patient, two plans were generated, one using our conventional Elekta Agility linac beam model and another using the Elekta MRI Linac (MRL) model. Both plans were generated on the average CT using similar dose constraints and a 5 mm PTV. The optimization was performed using our clinic's planning criteria, with normalization of the targets such that their V99% was equal to 99%. The OAR DVHs were compared for each patient. Results: The DVH plots revealed that there were limited differences when optimizing plans in the presence or absence of the magnetic field. The mean of the absolute differences, between the two planning types, in the equivalent uniform doses (EUDs) for the OARs were: 0.3 Gy (range of 0.0 ‐ 1.0 Gy) for the esophagus, 0.6 Gy (range of 0.1 – 1.9 Gy) for the heart, 0.5 Gy (range of 0.2 – 0.8 Gy) for the lungs, and 0.6 Gy (range of 0.2 – 1.5 Gy) for the spinal canal. Regarding the maximum doses to the serial organs, the mean of the differences were 3.0 Gy (esophagus) and 0.9 Gy (spinal canal). No trends in the differences were observed. Conclusion: This study has demonstrated that there were no major differences between plans optimized using a conventional linac and those optimized using an MRI linac with an orthogonal 1.5 T magnetic field. This is attributed to the consideration of the ERE in the optimization. This project was made possible with the financial support of Elekta
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".