# Preoperative inflammation-based immune prognostic nomogram in bladder cancer: a multicenter study

## TL;DR

This study develops a new tool to predict bladder cancer survival using pre-surgery blood markers and tumor features, helping guide treatment decisions.

## Contribution

A validated inflammation-based nomogram combining preoperative immune indices and pathological factors for bladder cancer prognosis.

## Key findings

- The nomogram includes NLR, PLR, AJCC stage, PNI, and LVI as independent predictors of survival.
- The model showed strong predictive accuracy with C-indices of 0.78 in training and 0.72 in validation.
- Calibration and decision curve analysis confirmed the model's clinical utility and reliability.

## Abstract

The systemic inflammatory response has been increasingly recognized as a crucial determinant of tumor progression and prognosis across various malignancies, including bladder cancer (BCa). Preoperative inflammation-based indices, which reflect the dynamic interaction between the tumor and host immune system, offer promising prognostic insights. However, existing studies have largely overlooked the synergistic integration of these indices with histopathological factors into a validated clinical tool for individualized survival prediction following radical cystectomy (RC).

We conducted a retrospective multicenter study involving 1,387 BCa patients who underwent RC at two tertiary hospitals in Yunnan, China, from 2014 to 2024. Key preoperative systemic inflammatory indices—including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI)—were extracted alongside clinical and pathological variables such as AJCC stage, perineural invasion (PNI), and lymphovascular invasion (LVI). Variable selection was performed via LASSO regression, and independent predictors were identified using multivariate Cox regression. A prognostic nomogram was developed and validated through concordance index (C-index), time-dependent ROC curves, calibration plots, and decision curve analysis (DCA).

The final nomogram included five independent predictors: NLR, PLR, AJCC stage, PNI, and LVI. The model demonstrated robust discrimination with C-indices of 0.78 in the training cohort and 0.72 in the external validation cohort. AUC values consistently exceeded 0.75 at 1-, 3-, and 5-year timepoints. Calibration plots showed excellent agreement between predicted and observed outcomes, and DCA confirmed meaningful clinical benefit across various threshold probabilities.

This study presents a validated, inflammation-based prognostic nomogram that combines preoperative hematologic indices with pathological features to predict overall survival in BCa patients undergoing RC. The model is non-invasive, cost-effective, and easy to implement using routine clinical data. Its strong predictive performance supports its application in preoperative counseling, individualized surveillance strategies, and decision-making regarding adjuvant therapy, contributing to more precise and personalized management in bladder cancer care.