Post-operative radiotherapy for oral cavity squamous cell carcinoma: Review of the data guiding the selection and the delineation of post-operative target volumes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To date, no consensus guidelines have been published that systematically guide delineation of primary and nodal Clinical Target Volumes (CTVs) in patients who require post-operative radiotherapy (PORT) for head and neck squamous cell carcinoma (HNSCC). As a result, significant individual, institutional and national variation exists in the way that CTVs are delineated in the post-operative setting, leading to considerable heterogeneity in radiotherapy treatment. METHODS: A multi-disciplinary group of experts was convened by the European Society for Radiotherapy and Oncology (ESTRO), including radiation oncologists from Europe, North America and Asia, as well as surgery, radiology and pathology representatives. Oral cavity squamous cell carcinoma (OCSCC), where surgery followed by PORT is the standard of care, was first selected for focus. The indications for PORT, and the influence of tumour subsite and stage on post-operative treatment volumes, were considered with reference to current evidence, and clinical experience within the group. RESULTS: We present clear recommendations regarding the indications for PORT in OCSCC, and propose a new classification of lateralised and non-lateralised OCSCC, to help guide the delineation of post-operative nodal CTVs. CONCLUSIONS: The evidence and expert opinion summarised in this manuscript provides the background and context required to underpin new international consensus guidelines for the delineation of primary and nodal CTVs for OCSCC in the post-operative setting.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".