Impact of radiobiological parameters on tumor control probability in rectal cancer using the Poisson linear quadratic model
Bibliographic record
Abstract
We explore several radiobiological parameters, specifically the tissue-specific linear quadratic α/β, the maximum normalized gradient of dose response γ, and the dose giving a 50% response probability for the tissue considered D 50 , looking at how these affect the computed Tumor Control Probabilities (TCPs) within the Poisson Linear Quadratic (LQ) model. A total of 3,584,000 TCP values were derived for 28 rectal cancer patients, utilizing two slightly different treatment plans: the short plan (50 Gy in 25 fractions) and the long plan (50.4 Gy in 28 fractions), along with 64,000 parameter combinations. TCP was calculated using the Poisson LQ model, which combines radiobiological parameters (α/β, γ, and D 50 ) with the voxel-based Equivalent Dose (EQD). Our analysis reveals that D 50 has the most significant influence, followed by γ, while α/β has minimal impact. Moreover, the influence of γ depends on the specific D 50 value employed. Furthermore, comparing TCPs obtained for short and long plans, these consistently show the TCP of the short plan to surpass that of the long plan when the same radiobiological parameter values are used. This study suggests that evaluating treatment plan efficacy through TCPs it is appropriate to use consistent parameter values within the Poisson Linear Quadratic model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".