SU‐E‐T‐633: Preparation and Planning of a VMAT Multi ‐ Arc Radiation Therapy Technique for Full Scalp Treatment
Bibliographic record
Abstract
Purpose: The target volume for angiosarcoma of the scalp encompasses the entire scalp. Full scalp radiotherapy (FSRT) requires careful design of required bolus, immobilization and marking of the field before the patient CT is acquired. A VMAT multi‐arc technique was designed to deliver FSRT for a patient with angiosarcoma of the scalp to a dose of 6000cGy in 25 fractions. Methods: A custom bolus helmet was fabricated from a 0.5 cm thick sheet of aquaplast material, which was molded to the patient's head. With the bolus helmet in place the patient was then positioned supine on a H&N immobilization board. A custom vaclock bag positioned on a standard headrest and a thermoplastic mask were used to immobilize the patient. Additional bolus to cover the remaining treatment area was attached to the mask. We acquired two CT scans of the patient's head, one in treatment position and an additional scan without the immobilization mask with wires marking the treatment area that the oncologist had delineated on the patient's skin. The second scan was registered to the first and used to define the treatment CTV. A four‐arc VMAT treatment planned using Varian‐Eclipse was optimized to cover the skin with a PTV margin while sparing the brain and limiting the dose to the optic apparatus and lacrimal glands. Daily treatment setup was verified using anterior and lateral kV on‐board‐imaging. To verify the treated dose, TLDs were positioned on the patient's scalp during one fraction. Results: With full dose coverage to the PTV, the mean dose to the brain was less than 24 Gy. The dose measured by the TLDs (mean difference 1%, standard deviation 4%)showed excellent agreement with the treatment planning calculation. Conclusion: FSRT delivered with a bolus helmet and a VMAT multi‐arc technique can be accurately delivered with high dose uniformity and conformality.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".