Preoperative Systemic Inflammatory Markers as a Significant Prognostic Factor After TURBT in Patients with Non-Muscle-Invasive Bladder Cancer.

Abstract

Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) have been widely proposed to have predictive value for the patient prognosis of many malignancies, including bladder cancer. However, the predictive value of their combination in non-muscle-invasive bladder cancer (NMIBC) is unclear. Cases of NMIBC patients who underwent transurethral resection of the bladder tumor were recruited from two tertiary public medical centers. A systemic inflammatory marker (SIM) score was calculated based on comprehensive consideration of NLR, PLR, and LMR. Recurrence-free survival (RFS) and progression-free survival (PFS) were estimated by Kaplan-Meier analysis. The Log rank test was used to compare differences between the groups. Cox regression was used to screen risk factors affecting RFS and PFS. Nomogram models were established and validated, and patients were stratified based on the model scores. The study dataset was grouped according to a 7:3 randomization, with the training cohort consisting of 292 cases and the validation cohort consisting of 124 cases. Cox regression analysis showed that SIM score is an independent predictor of RFS and PFS in NMIBC patients. The novel models were established based on the SIM score and other statistically significant clinicopathological features. The area under the curve (AUC) for predicting 1-, 2-, and 3-year RFS was 0.667, 0.689, and 0.713, respectively. The AUC for predicting 1-, 2-, and 3-year PFS was 0.807, 0.775, and 0.862, respectively. Based on the risk stratification, patients at high risk of recurrence and progression could be accurately identified. The established models were applied to the patient evaluation of the validation cohort, which proved the great performance of the novel models. The novel models based on the SIM score and clinicopathological characteristics can accurately predict the survival prognosis of NMIBC patients, and the models can be used by clinicians for individualized patient assessment and to assist in clinical decision-making.