Sentinel Node Necrosis Is a Negative Prognostic Factor in Patients with Nasopharyngeal Carcinoma: A Magnetic Resonance Imaging Study of 252 Patients
Bibliographic record
Abstract
Purpose: We explored the patterns of sentinel node metastasis and investigated the prognostic value of sentinel node necrosis (SNN) in patients with nasopharyngeal carcinoma (NPC), based on magnetic resonance imaging (MRI). Methods: This retrospective study enrolled 252 patients at our institution who had metastatic lymph nodes from biopsy-confirmed NPC and who were treated with definitive radiation therapy, with or without chemotherapy. All participants underwent MRI before treatment, and the resulting images were reviewed to evaluate lymph node status. The patients were divided into SNN and non-SNN groups. Overall survival (OS), tumour-free survival (TFS), regional relapse–free survival (RRFS), and distant metastasis–free survival (DMFS) were calculated by the Kaplan–Meier method, and differences were compared using the log-rank test. Factors predictive of outcome were determined by univariate and multivariate analysis. Results: Of the 252 patients, 189 (75%) had retropharyngeal lymph node metastasis, and 189 (75%) had level IIA or IIB lymph node necrosis. The incidence of snn was 43.4% (91 of 210 patients with lymph node metastasis or necrosis, or both). After a median follow-up of 54 months, the 5-year rates of OS, TFS, RRFS, and DMFS in the SNN and non-SNN groups were, respectively, 79.4% and 95.3%, 73.5% and 93.3%, 80.4% and 96.6%, and 75.5% and 95.3% (all p < 0.01). Age greater than 40 years, SNN, T stage, and N stage were significant independent negative prognostic factors for OS, TFS, RRFS, and DMFS. Conclusions: Metastatic retropharyngeal lymph nodes and necrotic level II nodes both seem to act as sentinels. Sentinel node necrosis is an negative prognostic factor in patients with NPC. Patients with snn have a worse prognosis.
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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 | 0.001 |
| 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".