Liquid Embolization Material Reduces the Delivered Radiation Dose: Clinical Myth or Reality?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To be radiopaque, BAVM embolization products must contain high-atomic-number materials, which may also attenuate photon beams delivered with radiosurgery. This "shielding effect" has been invoked to explain why radiation therapy may be less effective for previously embolized BAVMs. To evaluate the impact of embolization material on radiation dose, we measured and compared the dose delivered to the center of an AVM model, before and following embolization with various materials in a LINAC. MATERIALS AND METHODS: Two in vitro AVM models were constructed by drilling interconnected tubular perforations in plastic water phantoms to simulate nidal vessels. Phantoms were designed to allow the positioning of a radiation detector at their center. One model was embolized with Onyx 18 and a second model, with a combination of Indermil, Lipiodol, tungsten powder, and Onyx 18. The radiation delivered was compared between embolized and nonembolized controls following irradiation with a standard 250-cGy dose. RESULTS: The mean dose of radiation delivered to the model embolized with Onyx alone was 244 ± 5 cGy before and 246 ± 5 cGy following embolization. The mean dose of radiation delivered to the model embolized with various agents was 242 ± 5 cGy before, and 254 ± 5 cGy after embolization. CONCLUSIONS: Embolic material did not reduce the radiation dose delivered by a LINAC to the center of our experimental BAVM models. The shielding effect may be compensated by scattered and reflected radiation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".