Macroscopic sessile tumor architecture is a pathologic feature of biologically aggressive upper tract urothelial carcinoma.
Academic Article
Overview
abstract
OBJECTIVE: Macroscopic sessile tumor architecture was associated with adverse outcomes after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). Before inclusion in daily clinical decision-making, the prognostic value of tumor architecture needs to be validated in an independent, external dataset. We tested whether macroscopic tumor architecture improves outcome prediction in an international cohort of patients. MATERIAL AND METHODS: We retrospectively studied 754 patients treated with RNU for UTUC without neoadjuvant chemotherapy at 9 centers located in Asia, Canada, and Europe. Tumor architecture was macroscopically categorized as either papillary or sessile. Univariable and multivariable Cox regression analyses were used to address recurrence-free (RFS) and cancer-specific survival (CSS) estimates. RESULTS: Macroscopic sessile architecture was present in 20% of the patients. Its prevalence increased with advancing pathologic stage and it was significantly associated with established features of biologically aggressive UTUC, such as tumor grade, lymph node metastasis, lymphovascular invasion, and concomitant CIS (all P values < 0.02). The median follow-up for patients who were alive at last follow-up was 40 months (IQR: 18-75 months, range: 1-271 months). Two-year RFS and CSS for tumors with papillary architecture were 85% and 90%, compared with 58% and 66% for those with macroscopic sessile architecture, respectively (P values < 0.0001). On multivariable Cox regression analyses, macroscopic sessile architecture was an independent predictor of both RFS (hazard ratio {HR}: 1.5; P = 0.036) and CSS (HR: 1.5; P = 0.03). CONCLUSION: We confirmed the independent prognostic value of macroscopic tumor architecture in a large, independent, multicenter UTUC cohort. It should be reported in every pathology report and included in post-RNU predictive models in order to refine current clinical decision making regarding follow-up protocol and adjuvant therapy.
