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.