Anatomic prognostic factors and their potential roles in refining <scp>M1</scp> classification for de novo metastatic nasopharyngeal carcinoma
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To identify anatomic prognostic factors and their potential roles in refining M1 classification for de novo metastatic nasopharyngeal carcinoma (M1-NPC). MATERIALS AND METHODS: All M1-NPC treated with chemotherapy and/or radiotherapy between 2010 and 2019 from two centers (training and validation cohort) were included. The prognostic value of metastatic disease extent and involved organs for overall survival (OS) were assessed by several multivariable analyses (MVA) models. A new M1 classification was proposed and validated in a separate cohort who received immuno-chemotherapy. RESULTS: A total of 197 M1-NPC in the training and 307 in the validation cohorts were included for M1 subdivision study with median follow-up of 46 and 57 months. MVA model with "≤2 organs/≤5 lesions" as the definition of oligometastasis had the highest C-index (0.623) versus others (0.606-0.621). Patients with oligometastasis had better OS versus polymetastasis (hazard ratio [HR] 0.47/0.63) while liver metastases carried worse OS (HR 1.57/1.45) in MVA in the training/validation cohorts, respectively. We proposed to divide M1-NPC into M1a (oligometastasis without liver metastases) and M1b (liver metastases or polymetastasis) with 3-year OS of 66.5%/31.7% and 64.9%/35.0% in the training/validation cohorts, respectively. M1a subset had a better median progress-free survival (not reach vs. 17 months, p < 0.001) in the immuno-chemotherapy cohort (n = 163). CONCLUSION: Oligometastasis (≤2 organs/≤5 lesions) and liver metastasis are prognostic for M1-NPC. Subdivision of M1-NPC into M1a (oligometastasis without liver metastasis) and M1b (liver metastasis or polymetastasis) depicts the prognosis well in M1-NPC patients who received immuno-chemotherapy.
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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 | 0.001 |
| 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".