An International Expert Survey on the Indications and Practice of Radical Thoracic Reirradiation for Non-Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Thoracic reirradiation for non-small cell lung cancer with curative intent is potentially associated with severe toxicity. There are limited prospective data on the best method to deliver this treatment. We sought to develop expert consensus guidance on the safe practice of treating non-small cell lung cancer with radiation therapy in the setting of prior thoracic irradiation. METHODS AND MATERIALS: Twenty-one thoracic radiation oncologists were invited to participate in an international Delphi consensus process. Guideline statements were developed and refined during 4 rounds on the definition of reirradiation, selection of appropriate patients, pretreatment assessments, planning of radiation therapy, and cumulative dose constraints. Consensus was achieved once ≥75% of respondents agreed with a statement. Statements that did not reach consensus in the initial survey rounds were revised based on respondents' comments and re-presented in subsequent rounds. RESULTS: Fifteen radiation oncologists participated in the 4 surveys between September 2019 and March 2020. The first 3 rounds had a 100% response rate, and the final round was completed by 93% of participants. Thirty-three out of 77 statements across all rounds achieved consensus. Key recommendations are as follows: (1) appropriate patients should have a good performance status and can have locally relapsed disease or second primary cancers, and there are no absolute lung function values that preclude reirradiation; (2) a full diagnostic workup should be performed in patients with suspected local recurrence and; (3) any reirradiation should be delivered using optimal image guidance and highly conformal techniques. In addition, consensus cumulative dose for the organs at risk in the thorax are described. CONCLUSIONS: These consensus statements provide practical guidance on appropriate patient selection for reirradiation, appropriate radiation therapy techniques, and cumulative dose constraints.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".