Reirradiation dose constraints in clinical practice: Results of an international survey
Bibliographic record
Abstract
Introduction Despite the increasing frequency of reirradiation (reRT) in cancer treatment, a critical lack of reliable dose constraint data remains. This study addresses this gap by collating current reRT constraints used in clinical practice across multiple centers, facilitating the development of more consistent and safer reRT guidelines. Materials and methods A comprehensive survey collected data on reRT patient numbers, dose constraints, sources, and dose summation methods for 30 OARs. Information also included PRV margins, tissue recovery factors (TRF) with time intervals, α/β values, near-D max definitions, and dose constraints in EQD2Gy for first and reRT courses. The relative difference (X reRT ) between cumulative reRT and first course constraints was calculated. Constraints with data from at least 7 centers were included for further analysis. Results A median of 6 % of treatments in 17 participating centers were reRT. Most centers derived reRT constraints from the literature (81 %) or first course constraints (68 %). In total 209 cumulative near-D max values for 19 OARs fulfilled n ≥ 7, yielding a median inter-center variation of 21 % (IQR). While α/β values were relatively consistent, substantial variations were seen in near-D max volume definition, TRF, and PRV margins. The median X reRT was 26 %, primarily attributed to the TRF which had a median value of 23 %. Conclusions This multi-centre survey identified a concerning median inter-centre variation of 21% in cumulative reRT dose constraints, indicating substantial heterogeneity in current clinical practices. Further prospective studies with rigorous and standardized dose reporting are essential to refine reRT guidelines, enhancing patient safety and treatment efficacy.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".