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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".