PS1 - 189 A Universal Predictive Model for Dose Fall-Off in MLC-Based Stereotactic Brain Radiosurgery
Bibliographic record
Abstract
Predictive modeling of dose fall-off in radiosurgery could assist in clinical decision-making when prescribing a treatment plan with minimized toxicity risk. The purpose of this study is to develop a predictive dose fall-off model. Materials/Methods: We retrospectively reviewed treatment plans from 257 patients (365 lesions) with total doses ranging from 20 to 35Gy in 5 fractions. For each plan, we measured both total volume of the external contour (EXT) and BrainMinusPTV (BMP) receiving P=20% to P=80% of the prescription dose. The model has form y=Fa(PTV)b+/-delta. y=volume of EXT or BMP (cc’s); a and b are curve-fitting coefficients; PTV=total planning target volume (cc’s); F is an adjustment factor (>1) to account for number of targets; delta is the 95% prediction band. F, a, b, and delta were modeled such that dose-fall can be forecast for any PTV and dose level. Results: The model coefficients were as follows: Coefficient EXT BMP a 19927(100×P)exp(-2) 17122(100×P)exp(-2) b 0.42(100×P)exp(0.17) 0.63 F -0.0156×(100×P)+2.5517 delta 384467×(100×P)exp(-2.3159) The table can be used to determine the model for any P from 20% to 80%. Example: the EXT receiving 50%, P=0.5, a=8.0, b=0.82, F=1.8, delta=45. Thus, EXT-50=8(PTV0.82) or 1.8×8(PTV0.82) for 1-3 or >3 targets, respectively,+/-45cc’s. The model was verified against published values of dose fall-off from linacs. Conclusion: A predictive dose fall-off model was generated for linac-based radiosurgery. The model can be used for quality assurance or for inter-institutional comparisons. Ongoing work is being conducted to extend the model to a SRS cones system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".