Dosimetric Comparison of Fractionated Stereotactic Radiotherapy Plans With And Without Flattening Filter Beams of 6 MV And 10 MV Beams
Bibliographic record
Abstract
Introduction: It is necessary to understand the importance of different energies in Fractionated Stereotactic Radiotherapy (FSRT) plans for better outcome. The study objective is to compare FSRT plans with Flattening Filter (FF) and Flattening Filter Free (FFF) beams.Material and Methods: Twelve patients with primary Brain Metastasis (BM), were selected and given 25 Gy in five fractions for which 6FF beams were angled in double arc. The Planning Target Volume (PTV) and Organs at Risk (OARs) were assessed using dosimetric indices after each plan was replanned with 6 FFF, 10 FF, and 10 FFF energies. Treatment time (TT) and Monitor Units (MUs) were also compared. Additionally, we compared portal dosimetry for dose agreement across all plans using the gamma analysis criterion.Results: PTV parameters of created plans showed better values when compared to 6 FF plans, where the most significant is with FFF plans which include D98%, D80%, D2%, D50% and Dose Gradient Index values of 6FFF plans. Among OARs, the most significant is the V10 value of (Brain-PTV) as (46.77±43.9) and maximum dose values of optic chiasm, brainstem, and left lens in 6FFF plans. Among technical parameters, the 6FFF plan showed significant TT value of (3.06±1.0) with p-value 4.13E-05. Better gamma analysis passing rates were achieved with FFF beams.Conclusion: Linear accelerator-based FSRT delivery of BM using 6 FFF beam results in better dosimetric indices, OAR sparing, fastest treatment delivery, and energy conservation with reduced peripheral and out-of-field dose for higher treatment modalities like Rapid arc.
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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) | 0.003 | 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".