Background/aim: Staging of bladder cancer is crucial for optimal management of the disease. However, clinical staging is not perfectly accurate. The aim of this study was to derive a simple scoring system in prediction of pathological advanced muscle-invasive bladder cancer (MIBC).
Methods: Logistic regression and bootstrap methods were used to create an integer score for estimating the risk in prediction of pathological advanced MIBC using precystectomy clinicopathological data: demographic, initial transurethral resection (TUR) [grade, stage, multiplicity of tumors, lymphovascular invasion (LVI)], hydronephrosis, abdominal and pelvic CT radiography (size of the tumor, tumor base width), and pathological stage after radical cystectomy (RC). Advanced MIBC in surgical specimen was defined as pT3-4 tumor. Receiving operating characteristic (ROC) curve quantified the area under curve (AUC) as predictive accuracy. Clinical usefulness was assessed by using decision curve analysis.
Results: This single-center retrospective study included 233 adult patients with BC undergoing RC at the Military Medical Academy, Belgrade. Organ confined disease was observed in 101 (43.3%) patients, and 132 (56.7%) had advanced MIBC. In multivariable analysis, 3 risk factors most strongly associated with advanced MIBC: grade of initial TUR [odds ratio (OR) = 4.7], LVI (OR = 2), and hydronephrosis (OR = 3.9). The resultant total possible score ranged from 0 to 15, with the cut-off value of > 8 points, the AUC was 0.795, showing good discriminatory ability. The model showed excellent calibration. Decision curve analysis showed a net benefit across all threshold probabilities and clinical usefulness of the model.
Conclusion: We developed a unique scoring system which could assist in predicting advanced MIBC in patients before RC. The scoring system showed good performance characteristics and introducing of such a tool into daily clinical decision-making may lead to more appropriate integration of perioperative chemotherapy. Clinical value of this model needs to be further assessed in external validation cohorts.