Recommendations for postoperative radiotherapy in head & neck squamous cell carcinoma in the presence of flaps: A GORTEC internationally-reviewed HNCIG-endorsed consensus
Bibliographic record
Abstract
INTRODUCTION: Head and neck reconstructive surgery using a flap is increasingly common. Best practices and outcomes for postoperative radiotherapy (poRT) with flaps have not been specified. We aimed to provide consensus recommendations to assist clinical decision-making highlighting areas of uncertainty in the presence of flaps. MATERIAL AND METHODS: Radiation, medical, and surgical oncologists were assembled from GORTEC and internationally with the Head and Neck Cancer International Group (HNCIG). The consensus-building approach covered 59 topics across four domains: (1) identification of postoperative tissue changes on imaging for flap delineation, (2) understanding of tumor relapse risks and target volume definitions, (3) functional radiation-induced deterioration, (4) feasibility of flap avoidance. RESULTS: Across the 4 domains, international consensus (median score ≥ 7/9) was achieved only for functional deterioration (73.3%); other consensus rates were 55.6% for poRT avoidance of flap structures, 41.2% for flap definition and 11.1% for tumor spread patterns. Radiation-induced flap fibrosis or atrophy and their functional impact was well recognized while flap necrosis was not, suggesting dose-volume adaptation for the former. Flap avoidance was recommended to minimize bone flap osteoradionecrosis but not soft-tissue toxicity. The need for identification (CT planning, fiducials, accurate operative report) and targeting of the junction area at risk between native tissues and flap was well recognized. Experts variably considered flaps as prone to tumor dissemination or not. Discrepancies in rating of 11 items among international reviewing participants are shown. CONCLUSION: International GORTEC and HNCIG-endorsed recommendations were generated for the management of flaps in head and neck radiotherapy. Considerable knowledge gaps hinder further consensus, in particular with respect to tumor spread patterns.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".