Poster ‐ 47: A parametrized prediction model of rectal toxicity in focal SBRT of low risk prostate cancer
Bibliographic record
Abstract
There has been a recent trend towards watchful waiting in place of intervention for early stage prostate cancer (CaP). However, this approach can allow for disease progression, and subsequent whole‐gland therapies such as prostatectomy and whole gland irradiation can result in functional deficits or rectal toxicities or both. A controversial alternative approach for this patient cohort is the use of focal therapy, where the treatment is focussed on an identified dominant index lesion (DIL). This work aims to investigate the treatment parameters for focal SBRT of the prostate under which clinically acceptable rectal NTCP levels can be achieved. For each of 25 low risk CaP patients, a hypothetical 2 cc DIL was modeled in the right‐posterior quadrant of the prostate, and was used to build a PTV as the target for SBRT simulation. An SBRT prescriptions of 41 Gy and 37 Gy in 5 fractions were chosen, corresponding to the boost levels used in previous CaP dose escalation studies. DVH data were exported and used to calculate rectal NTCP values based on the Lyman‐Kutcher‐Burman (LKB) model using the QUANTEC reccommended model parameters. Rectal NTCP dependence on DIL‐to‐rectum separation, dose level, and DIL volume were investigated. The final goal of this ongoing work is to create a map of the maximum allowable prescription dose for a given patient geometry that achieves a clinically acceptable rectal NTCP level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".