Cumulative dose evaluation in clinical reirradiation – Consensus guidance on technical considerations by the ESTRO reirradiation focus group
Bibliographic record
Abstract
Reirradiation is increasingly used, but its safe application requires accurate evaluation of cumulative doses to organs at risk. Methods of cumulative dose evaluation vary widely, from simple summation of prescription doses to complex three-dimensional (3D) dose summation methodologies, yet best practices remain undefined. This guidance, endorsed by the ESTRO Reirradiation Focus Group, provides detailed recommendations on the technical aspects of 3D dose summation; appropriate alternatives when it is unfeasible, suboptimal, or requires special considerations; and general guidance on uncertainty evaluation, resource management, and implications for treatment delivery. The writing group (n = 15) analysed scenarios ranging from full 3D dose summation to situations with incomplete data and unacceptable image registration, and developed recommendations through structured expert discussion and a two-step voting process. Key discussion points included: dose mapping (with rigid and deformable image registration), radiobiological corrections (particularly equieffective dose rescaling, such as EQD2Gy and BED), uncertainties, role of documentation, quality control, and peer review. Thirty-five statements reached consensus within both the writing group and the full ESTRO Physics Reirradiation Working Group (n = 34, 66 % response rate). Altogether, this guidance offers a practical framework for standardizing the technical aspects of cumulative dose evaluation to support clinical decision making and improve safety and effectiveness of reirradiation, while reducing institutional variability. It also highlights ongoing needs for research into advanced dose mapping, image registration, and integration of uncertainty analyses.
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 | 0.209 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".