Predicting pathological upstaging after radical nephroureterectomy in patients with upper tract urothelial carcinoma: results from a multicenter cohort study.

Abstract

Background: Despite the availability of advanced imaging technologies, it remains difficult to achieve sufficient staging accuracy to ensure a tailored treatment strategy for patients with upper tract urothelial carcinoma (UTUC). The aim of the study was to identify preoperative risk factors for tumor upstaging in patients with UTUC initially staged as clinical T2 or lower and to analyze these factors separately for renal pelvic cancer and ureteral cancer.

Methods: This retrospective study included data from patients with UTUC who underwent nephroureterectomy. Among them, patients who underwent a staging evaluation using computed tomography urography within 90 days before surgery were selected. Various preoperative factors were evaluated, and multivariate logistic regression analyses were conducted to identify predictors of pathological tumor upstaging.

Results: The study included 496 patients, of whom 392 were diagnosed with clinical T2 stage or lower. Among these, 125 patients (31.9%) were upstaged to pathological T3 or T4 disease. Multivariate analysis identified positive voided urine cytology [hazard ratio (HR) =2.94, P<0.001] and tumor size ≥30 mm (HR =1.90, P=0.008) as independent predictors of upstaging. Subgroup analysis showed that positive voided urine cytology (HR =2.71, P=0.004) and tumor size ≥30 mm (HR =3.39, P=0.001) were significant risk factors for renal pelvic cancer. In contrast, significant predictors for ureteral cancer included positive voided urine cytology (HR =3.11, P=0.003) and hydronephrosis (HR =2.69, P=0.03).

Conclusions: Positive voided urine cytology and larger tumor size were significant predictors of pathological upstaging in patients with UTUC. Differences in the risk factors between renal pelvic and ureteral cancers highlight the need for tailored preoperative evaluations and management strategies. Further studies are required to refine these predictive models and improve clinical decision-making.