Combining imaging and ureteroscopy variables in a preoperative multivariable model for prediction of muscle-invasive and non-organ confined disease in patients with upper tract urothelial carcinoma

Objective: • To create a preoperative multivariable model to identify patients at risk of muscle-invasive (pT2+) upper tract urothelial carcinoma (UTUC) and/or non-organ confined (pT3+ or N+) UTUC (NOC-UTUC) who potentially could benefit from radical nephroureterectomy (RNU), neoadjuvant chemotherapy and/or an extended lymph node dissection. Patients and Methods: • We retrospectively analysed data from 324 consecutive patients treated with RNU between 1995 and 2008 at a tertiary cancer centre. • Patients with muscle-invasive bladder cancer were excluded, resulting in 274 patients for analysis. • Logistic regression models were used to predict pT2+ and NOC-UTUC. Pre-specified predictors included local invasion (i.e. parenchymal, renal sinus fat, or periureteric) on imaging, hydronephrosis on imaging, high-grade tumours on ureteroscopy, and tumour location on ureteroscopy. • Predictive accuracy was measured by the area under the curve (AUC). Results: • The median follow-up for patients without disease recurrence or death was 4.2 years. • Overall, 49% of the patients had pT2+, and 30% had NOC-UTUC at the time of RNU. • In the multivariable analysis, only local invasion on imaging and ureteroscopy high grade were significantly associated with pathological stage. • AUC to predict pT2+ and NOC-UTUC were 0.71 and 0.70, respectively. Conclusions: • We designed a preoperative prediction model for pT2+ and NOC-UTUC, based on readily available imaging and ureteroscopic grade. • Further research is needed to determine whether use of this prediction model to select patients for conservative management vs RNU, neoadjuvant chemotherapy, and/or extended lymphadenectomy will improve patient outcomes.