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

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: NMIBC; bladder cancer; nomogram; risk factor; systemic inflammatory markers; tumor recurrence.

Figure 1

The Kaplan-Meier analysis of RFS stratified by (A) NLR, (B) PLR, (C) LMR; and the Kaplan-Meier analysis of PFS stratified by (D) NLR, (E) PLR, (F) LMR in the training cohort.

Figure 2

The Kaplan-Meier analysis of (A) RFS and (B) PFS stratified by SIM score in the training cohort.

Figure 3

(A) The nomogram for predicting RFS after TURBT for NMIBC. (B) Time-dependent ROC curves of the nomogram for predicting RFS in the training cohort. (C) Calibration plot of the nomogram by bootstrapping with 1000 resamples for predicting RFS in the training cohort. (D) Decision-curve analyses demonstrating the net benefit associated with the use of the model for predicting RFS.

Figure 4

(A) The nomogram for predicting PFS after TURBT for NMIBC. (B) Time-dependent ROC curves of the nomogram for predicting PFS. (C) Calibration plot of the nomogram by bootstrapping with 1000 resamples for predicting PFS. (D) Decision-curve analyses demonstrating the net benefit associated with the use of the model for predicting PFS.

Figure 5

(A) Time-dependent ROC curves of the nomogram for predicting RFS in the validation cohort in the validation cohort. (B) Calibration plot of the nomogram by bootstrapping with 1000 resamples for predicting RFS in the validation cohort in the validation cohort. (C) Time-dependent ROC curves of the nomogram for predicting PFS in the validation cohort in the validation cohort. (D) Calibration plot of the nomogram by bootstrapping with 1000 resamples for predicting PFS in the validation cohort in the validation cohort.

Figure 6

The Kaplan-Meier curves of low-risk and high-risk groups based on the prediction of the nomogram models for (A) RFS and (B) PFS.