FLASH-enabled proton SBRT for a challenging case of spine metastasis
Bibliographic record
Abstract
Abstract Objective. The FLASH effect, characterized by potential sparing of organs at risk (OARs) through ultra-high dose rate (DR) irradiation, has garnered significant attention for its capability to address indications previously untreatable at conventional DRs with hypofractionated schemes. While considerable biological research is needed to understand the FLASH effect and determine the FLASH modifying factors (FMF) for individual OARs, treatment planning studies have also emerged. This study evaluates the feasibility of achieving FLASH conditions in proton stereotactic body radiotherapy for spine metastases and establishes the required FMFs under different fractionation regimens. Approach. A conformal FLASH Proton SBRT plan was generated for a patient with spine metastasis in a research version of RayStation11B (RaySearch laboratories AB, Stockholm) on an IBA Proteus Plus system. Two oblique posterior beams were used in the plan. The prescribed dose to the CTV was set according to 3 different fractionation regimens: 5 fractions (fx) of 7 Gy, 8 fx of 5 Gy, and 10 fx of 4.2 Gy. Spot filtering and sorting techniques were applied to maximize the 5% pencil beam scanning DR in the spinal cord (SC). The FLASH effect was assumed to be observed within irradiated regions above 40 Gy s−1 and 4 Gy per fraction. Main results. The generated plans successfully ensure robust target coverage in each fraction. The volume of SC that does not comply with the clinical goal adheres to the FLASH effect conditions in each fraction. Depending on the aforementioned fractionation schemes used, a FMF of approximately 0.6 to 0.8 is necessary to enable such treatment in FLASH conditions. Significance. This study indicates that treating challenging spine metastases with protons using FLASH delivery is technically feasible. However, clinical viability depends on optimistic parameters to trigger the FLASH effect and FMF values below 0.8, which are not yet guaranteed given current research.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".