SU‐F‐T‐432: Magnetic Field Dose Effects for Various Radiation Beam Geometries for Patients Treated with Hypofractionated Partial Breast Irradiation
Bibliographic record
Abstract
Purpose: Hypofractionated partial breast irradiation (HPBI) is being used at our clinic to treat inoperable breast cancer patients who have advanced disease. We are investigating how these patients could benefit from being treated in an MRI‐linac, where real‐time daily MRI tumor imaging and plan adaptation would be possible. As a first step, this study evaluates the dosimetric impact of the magnetic field for different radiation beam geometries on relevant OARs. Methods: Five patients previously treated using HPBI were selected. Six treatment plans were generated for each patient, evaluating three beam geometries (VMAT, IMRT, 3DCRT) with and without B0=1.5 T. The Monaco TPS was used with the Elekta MRI‐Linac beam model, where the magnetic field is orthogonal to the radiation beam. All plans were re‐scaled to the same isocoverage with a prescription of 40Gy/5 to the PTV. Plans were evaluated for the effect of the magnetic field and beam modality on skin V3 0, lung V2 0 and mean heart dose. Results: Averaged over all patients, skin V3 0for 3DCRT was higher than VMAT and IMRT (by +22% and +21%, with B0‐ON). The magnetic field caused larger increases in skin V3 0for 3DCRT (+8%) than VMAT (+3%) and IMRT (+4%) compared with B0‐OFF. With B0‐ON, 3DCRT had a markedly lower mean heart dose than VMAT (by 538cGy) and IMRT (by 562cGy); for lung V2 0, 3DCRT had a marginally lower dose than VMAT (by −2.2%) and IMRT (also −2.2%). The magnetic field had minimal effect on the mean heart dose and lung V2 0 for all geometries. Conclusion: The decreased skin dose in VMAT and IMRT can potentially mitigate the effects of skin reactions for HPBI in an MRI‐linac. This study illustrated that more beam angles may result in lower skin toxicity and better tumor conformality, with the trade‐off of elevated heart and lung doses. We are receiving funding support from Elekta
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".