Sci-Fri PM: Topics - 04: What if bystander effects influence cell kill within a target volume? Potential consequences of dose heterogeneity on TCP and EUD on intermediate risk prostate patients
Bibliographic record
Abstract
In vitro evidence has suggested that radiation induced bystander effects may enhance non-local cell killing which may influence radiotherapy treatment planning paradigms. This work applies a bystander effect model, which has been derived from published in vitro data, to calculate equivalent uniform dose (EUD) and tumour control probability (TCP) and compare them with predictions from standard linear quadratic (LQ) models that assume a response due only to local absorbed dose. Comparisons between the models were made under increasing dose heterogeneity scenarios. Dose throughout the CTV was modeled with normal distributions, where the degree of heterogeneity was then dictated by changing the standard deviation (SD). The broad assumptions applied in the bystander effect model are intended to place an upper limit on the extent of the results in a clinical context. The bystander model suggests a moderate degree of dose heterogeneity yields as good or better outcome compared to a uniform dose in terms of EUD and TCP. Intermediate risk prostate prescriptions of 78 Gy over 39 fractions had maximum EUD and TCP values at SD of around 5Gy. The plots only dropped below the uniform dose values for SD ∼ 10 Gy, almost 13% of the prescribed dose. The bystander model demonstrates the potential to deviate from the common local LQ model predictions as dose heterogeneity through a prostate CTV is varies. The results suggest the potential for allowing some degree of dose heterogeneity within a CTV, although further investigations of the assumptions of the bystander model are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".