Pan-Immune-Inflammation Value as a Predictor of Long-Term Outcomes in Patients with Urothelial Carcinoma of the Bladder: A Pilot Study
Bibliographic record
Abstract
Background: Urothelial carcinoma of the bladder (UCB) demonstrates considerable heterogeneity, with markedly varying outcomes between non–muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). The pan-immune-inflammation value (PIV), derived from routine hematological parameters, has emerged as a novel biomarker reflecting systemic inflammation and immune dysregulation. This pilot, exploratory analysis evaluated the prognostic relevance of the PIV in UCB and contextualized PIV against other inflammation-based indices. Methods: We retrospectively analyzed 119 patients with histologically confirmed UCB who were treated between 2019 and 2024. PIV was calculated as (neutrophils × platelets × monocytes) ÷ lymphocytes. Additional indices included the NLR, SII, SIRI, and PLR. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan–Meier analysis, and prognostic factors were assessed using Cox regression. Results: Among 119 patients (median age, 72 years; 88% male), 68 were diagnosed with NMIBC and 51 with MIBC. Elevated PIV levels were significantly associated with NMIBC progression to MIBC (p = 0.028) and strongly correlated with NLR, SII, SIRI, and PLR. Patients with high PIV exhibited shorter OS (24 vs. 45 months) and PFS (20 vs. 35 months) than those with low patients (p < 0.001). Although the prognostic value was evident in the univariate analyses, PIV did not retain significance in multivariate models. Conclusion: Elevated PIV levels predict adverse survival outcomes and progression in UCB, underscoring its potential as a cost-effective and accessible biomarker for risk stratification. Prospective validation in larger cohorts is warranted to confirm its role in personalized patient management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".