Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature

Abstract

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

Reduce bladder cancer recurrence in patients treated for upper urinary tract urothelial carcinoma: The REBACARE-trial

“…The exclusion criteria of a previous UCB will limit the inclusion rate of the REBACARE trial as the majority of patients with urothelial carcinoma of the UUT are known to have had one or more episodes of UCB in their history. However, this was necessary, because it is known that these patients are at much higher risk to develop a subsequent bladder recurrence following surgery for urothelial carcinoma of the UUT [ 11 , 24 , 36 ]. Including these patients will potentially jeopardize the outcome of this trial because it will have an impact on the primary endpoint of this trial.…”

“…Several studies have assessed individual patient risk factors for oncologic outcomes [ 5 , 7 , 12 , 24 , 25 ]. Lughezzani et al and Mathieu et al identified tumor stage and grade to be the most significant factors in oncological outcome [ 5 , 26 ].…”

“…Predictive nomograms are used widely in urology to help patients counseling and complex decision-making regarding treatment, but none of these to date have been developed based on prospective data and none have achieved widespread routine use, due to low level of evidence and lack of external validation. In a meta-analysis by Mbeutcha et al on predictive models for the treatment of urothelial carcinoma of the UUT [ 24 ], a positive predictive value of 89% was achieved when combining hydronephrosis, ureteroscopic grade and urinary cytology for prediction of advanced-stage of UUT urothelial carcinoma [ 6 ]. If all three were negative, the negative predictive value was 100%.…”

“…In both retrospective and prospective studies, a high proliferation index as assessed by Ki-67 expression was associated with disease recurrence and cancer-specific survival in urothelial carcinoma of the UUT [ [29] , [30] , [31] , [32] , [33] ]. Furthermore, alterations in the mTOR - pathway , and the genes HER2, BCAT1, CDCA5 and p53 might play a role in the prognosis of high grade urothelial carcinoma of the UUT, but the impact of these biomarkers hasn't been sufficiently validated because of the small portion of samples in single-institution cohorts [ 24 ].…”

Predicting the biological behavior of non-muscle-invasive bladder cancer: from histology to molecular taxonomy

“…Recently, it has been clarified that tumors with similar morphological features can have distinct genetic profiles and may belong to different molecular subgroups with different biological behavior ( 1 ). Interestingly, many attempts in these last years have demonstrated the promising predictive value of molecular markers in NMIBC ( 1 , 6 ). For example, a so-called “molecular grade” based on FGFR3 mutation and MIB-1 expression has been proposed and validated, identifying three subgroups at favorable (mutation; normal expression), intermediate (no mutation; normal expression or mutation; high expression) or poor (no mutation; high expression) prognosis ( 5 ).…”