Prognostic role of the systemic immune–inflammation index in upper tract urothelial carcinoma treated with radical nephroureterectomy: results from a large multicenter international collaboration
Bibliographic record
Abstract
PURPOSE: To investigate the prognostic role of the preoperative systemic immune-inflammation index (SII) in patients with upper tract urothelial carcinoma (UTUC) treated with radical nephroureterectomy (RNU). MATERIALS AND METHODS: We retrospectively analyzed our multi-institutional database to identify 2492 patients. SII was calculated as platelet count × neutrophil/lymphocyte count and evaluated at a cutoff of 485. Logistic regression analyses were performed to investigate the association of SII with muscle-invasive and non-organ-confined (NOC) disease. Cox regression analyses were performed to investigate the association of SII with recurrence-free, cancer-specific, and overall survival (RFS/CSS/OS). RESULTS: Overall, 986 (41.6%) patients had an SII > 485. On univariable logistic regression analyses, SII > 485 was associated with a higher risk of muscle-invasive (P = 0.004) and NOC (P = 0.03) disease at RNU. On multivariable logistic regression, SII remained independently associated with muscle-invasive disease (P = 0.01). On univariable Cox regression analyses, SII > 485 was associated with shorter RFS (P = 0.002), CSS (P = 0.002) and OS (P = 0.004). On multivariable Cox regression analyses SII remained independently associated with survival outcomes (all P < 0.05). Addition of SII to the multivariable models improved their discrimination of the models for predicting muscle-invasive disease (P = 0.02). However, all area under the curve and C-indexes increased by < 0.02 and it did not improve net benefit on decision curve analysis. CONCLUSIONS: Preoperative altered SII is significantly associated with higher pathologic stages and worse survival outcomes in patients treated with RNU for UTUC. However, the SII appears to have relatively limited incremental additive value in clinical use. Further study of SII in prognosticating UTUC is warranted before routine use in clinical algorithms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".