SU‐E‐T‐01: Applications of 6MV FFF Photon Beams in Optimizing Radiobiological Response for Respiratory‐Gated Liver SBRT
Bibliographic record
Abstract
PURPOSE: To establish a radiobiological basis for gated stereotactic body radiotherapy of primary and metastatic liver cancers using volumetric arc radiotherapy in a flattening filter free (FFF) mode. METHODS: Human cervical carcinoma, SiHa, non-small cell lung carcinoma, H460, and Chinese hamster V79 cells were irradiated in a water bath with 6MV photons from a Varian TrueBeam linear accelerator. To establish dose-response and its sensitivity to dose rate following acute irradiation, doses of 2, 4, 6, 8 and 10 Gy were delivered in FFF mode at 400 and 1200 MU/min. To investigate whether removal of the flattening filter affects cell response, doses of 5 and 10 Gy were delivered to SiHa and H460 cells in FFF and filtered modes at 400 MU/min. Finally, to assess the effect of protracting dose delivery by gating, a dose of 10 Gy was delivered to SiHa and H460 cells acutely and also over 15, 30 and 60 min. RESULTS: Dose-response over doses examined was independent of dose rate in FFF mode. Differences in cell survival following irradiation in FFF and filtered modes were not significant. However a significant increase in survival for both H460 and SiHa cells was observed for 15 min split-dose irradiation compared to acute irradiation but further increase in irradiation time to 60 min did not affect cell survival. CONCLUSIONS: Dose rate and presence of a flattening filter showed no effect on cell survival, however, survival was significantly affected when dose delivery time was protracted to that typical of conformal field therapy. Volumetric arc based gated SBRT may be beneficial for tumor cell kill, though the gating window and duty cycle have to be balanced against the effect of dose delivery protraction. Research Support (Varian Medical Systems).
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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 | 0.000 | 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".