Risk stratification and prognostication of renal cell carcinoma.
OBJECTIVE
To review the most recent data on prognostic factors and describe the characteristics and prognostic accuracy of the most important prognostic systems available to predict the risk of recurrence, progression, and mortality in patients with renal cell carcinoma (RCC).


METHODS
The study was based on a non-systematic review of literature.


RESULTS
Clinical (performance status, and mode of presentation), anatomical (size and extension of the primary tumor, lymph node involvement, and distant metastasis), and histological factors (histological subtypes, nuclear grade, and tumor necrosis) are the most largely evaluated prognostic factors in RCC. Valuable prognostic accuracy has been shown for several laboratory parameters (erythrocyte sedimentation rate, platelet count, serum calcium, hemoglobin, and lactate dehydrogenase levels) and a few genetical and molecular markers (CAIX, B7-H1, and B7-H4). A few integrating systems have been proposed and validated, integrating both clinical and pathological (UCLA Integrating Staging Systems, Kattan nomogram, and Sorbellini nomogram) or only pathological variables (SSIGN score).


CONCLUSIONS
Several large and methodologically consistent studies have been published. The chance to integrate the data derived from each prognostic factor into prognostic algorithms and scores has allowed improving significantly the stratification of the prognosis of patients with RCC. The currently available prognostic systems can be further improved through the inclusion of molecular and genetic variables. Integrating prognostic systems should be used to design randomized controlled trials (RCTs), which will evaluate the efficacy of the new-targeted therapies in either neoadjuvant, adjuvant, or salvage treatments of patients with RCC.
Ficarra
Vincenzo
V
Department of Oncological and Surgical Sciences, Urology Clinic, University of Padua, Monoblocco Ospedaliero - IV Floor, Via Giustiniani 2, 35128 Padua, Italy. vincenzo.ficarra@unipd.it
Galfano
Antonio
A
Novara
Giacomo
G
Iafrate
Massimo
M
Brunelli
Matteo
M
Secco
Silvia
S
Cavalleri
Stefano
S
Martignoni
Guido
G
Artibani
Walter
W
D000465:Algorithms; D002292:Carcinoma, Renal Cell / Q000175:diagnosis* / Q000473:pathology*; D018450:Disease Progression; D006801:Humans; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D009367:Neoplasm Staging; D011379:Prognosis