Can radiological changes in lymph node volume during treatment predict success of radiation therapy in patients with locally advanced head and neck squamous cell carcinoma?
Bibliographic record
Abstract
BACKGROUND: Assessment of nodal response after radiotherapy (RT) for head and neck squamous cell carcinoma is difficult, as both CT and positron emission tomography scanning have limited predictive value for residual disease. We sought to measure changes in nodal volume during RT to determine whether such changes are predictive of nodal disease control. METHODS: Patients with locally advanced head and neck squamous cell carcinoma treated with 70 Gy of radical RT (±chemotherapy or anti-epidermal growth factor receptor (EGFR) antibodies) were eligible. Baseline pre-RT scans and cone-beam CT scans done at the outset of treatment and at weeks 3, 5 and 7 (cone-beam CTs # 1, 2, 3 and 4, respectively) were deformably coregistered, and 3D nodal volumes were measured. RESULTS: Thirty-eight eligible patients were identified. The main primary tumour site was oropharyngeal; most patients had stage IVa disease. Twenty-seven patients received concurrent platinum-based chemotherapy, 10 received only an EGFR inhibitor with RT and one received RT alone. Twelve patients had a failure in the neck. After week 1 of treatment, a 4% mean decrease in nodal volume was observed, increasing to 40% at week 7. Platinum-based chemotherapy achieved significantly greater decreases in nodal volume than EGFR inhibitors (44 vs. 25%; P = 0.026). Advanced tumour stage predicted neck failure (P = 0.002), but nodal volumes did not correlate with neck control. CONCLUSIONS: Changes in nodal volume are minimal initially during RT but accelerate during the latter weeks of therapy. This study suggests that chemotherapy achieves a greater decrease in nodal volume than EGFR inhibitors and that nodal changes do not predict disease control in the neck.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".