SU‐GG‐T‐439: Dosimetric Comparison of Compensator and Multi‐Leaf Collimator (MLC) Based Intensity Modulated Radiosurgery (IMRS)
Bibliographic record
Abstract
Purpose: Multi‐leaf collimator (MLC) based intensity‐modulated radiosurgery (IMRS) often results in large number of monitor units (MU) for patients with multiple brain lesions. Compensator based IMRS, however, may dramatically reduce MU. The purpose of this study is to quantify the reduction of MU for IMRS of multiple brain lesions using solid tissue compensators. Method and Materials: Patients with multiple brain tumors were selected for our study. For each patient, Varian Eclipse TPS was used to generate an MLC based IMRS plan consisting of 10–11 coplanar beams. The prescription dose for a typical IMRS treatment is 1800–2000 cGy delivered in 1 fraction using a 6 MV photon beam. IMRS plans were generated on 2 patients. The optimal fluence maps from IMRS plan were exported to the compensator generating system to generate compensators for each field. The compensator files are imported back to Eclipse to calculate MUs for the compensator fields. Eclipse TPS was modified to allow compensator based planning and evaluation inside Eclipse. Finally, we compared MLC and compensator plans in terms of MUs and target and normal structure coverage. Results: Compensators offer superior resolution compared to MLCs and are easier and faster to plan. DVH analysis from both patients shows adequate target coverage for both IMRS and compensator plans. More sparing of normal tissues in compensator plan was observed sometimes. The MUs were reduced by factors of more than 3 compared to an MLC based IMRS plan. Conclusion: Compensator based IMRS can dramatically reduce the number of MU needed for multiple brain lesion radiosurgery as compared to an MLC based IMRS plan while preserve prescribed dose coverage. Conflict of Interest: This work is partially supported by .decimal Inc.
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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 | 0.000 |
| 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".