SU-FF-T-371: Equivalent Stochastic Dose (ESD): Quantifying the Impact of Dose Uncertainties On Radiotherapy Treatment Plans
Bibliographic record
Abstract
Purpose: To study the impact of dose uncertainties on radiotherapy treatment plans through the use of survival fraction. Method and Materials: We considered the case, where the total dose to a volume element has an associated uncertainty. To distinguish between spatial and probabilistic dose heterogeneities, we define equivalent stochastic dose (ESD) as the dose that results in a mean survival fraction for a randomly deposited dose to a volume. For a probability density function, f(D), that represents the dose to a voxel, SF(ESD) can be calculated using the convolution technique. In the case where the probability density function follows a Gaussian distribution, an analytic expression has been derived for SF(ESD). The expression has been verified using the Monte Carlo method for various radiosensitivities and α /β ratios. Results: The results show that survival fraction increases with an increased dose uncertainty and was found to be dependent on the radiobiological parameters. Using the analytic expression for SF(ESD), dose uncertainty can be evaluated for any voxel or group of voxels in the dose matrix, including those in an OAR and/or PTV. For spatially Gaussian dose distributions a statistical uncertainty of 2% was found to result in an average equivalent uniform stochastic dose (EUSD) of 98% with respect to the modelled mean dose. As the dose uncertainty increased beyond 5% the ratio of EUSD to prescribed dose dropped rapidly. Conclusion: In this work, statistical and physical dose uncertainties have been combined in a formalism that concurrently quantifies the impact of each on survival fraction. The effect of dose uncertainty can easily be evaluated for any voxel in the dose matrix including the PTV and OAR volumes. This can be useful in the assessment of planned treatments when dose uncertainty estimates are known.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".