Journal
BJU INTERNATIONAL
Volume 109, Issue 1, Pages 77-82Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1464-410X.2011.10288.x
Keywords
nephroureterectomy; prognosis; renal pelvis; transitional cell carcinoma; urothelial carcinoma
Categories
Funding
- Sidney Kimmel Center for Prostate and Urologic Cancers
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available