Total Mucosal Irradiation with Intensity-modulated Radiotherapy in Patients with Head and Neck Carcinoma of Unknown Primary: A Pooled Analysis of Two Prospective Studies
Bibliographic record
Abstract
AIMS: To determine the clinical outcomes of an intensity-modulated radiotherapy technique for total mucosal irradiation (TM-IMRT) in patients with head and neck carcinoma of unknown primary (HNCUP). MATERIALS AND METHODS: A single-centre prospective phase II trial design was used in two sequential studies to evaluate TM-IMRT for HNCUP. Patients were investigated for primary tumour site using examination under anaesthetic and biopsies, computed tomography ± magnetic resonance imaging (MRI) or 18-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT). Patients received IMRT to the potential primary tumour sites and elective cervical nodes. Concomitant chemotherapy was used in patients who received primary radiotherapy or those with nodal extracapsular extension. RESULTS: Thirty-six patients with HNCUP were recruited; 72% male. Twenty-five patients (69.4%) had p16-positive disease. Two year mucosal and local nodal control rates were 97.1% (95% confidence interval 91.4-100) and 89.8% (78.4-100), respectively. One mucosal primary was detected 7.3 months after TM-IMRT and three patients died from recurrent/metastatic squamous cell carcinoma of the head and neck. Twelve patients (33%) developed grade 3 (Late Effects in Normal Tissue-Subjective, Objective, Management and Analytical; LENT-SOMA) dysphagia with a 1 year enteric tube feeding rate of 2.7%. The high-grade subjective xerostomia rate (LENT-SOMA) at 24 months after IMRT was 15%. CONCLUSIONS: At a median follow-up of 36.1 months, the use of TM-IMRT was associated with good local control. Toxicity was comparable with previously reported TM-IMRT regimens encompassing similar mucosal volumes.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".