Predicting cancer-control outcomes in patients with renal cell carcinoma.
OBJECTIVE
An increasing number of models are becoming available for patients with either suspected or established renal cell carcinoma (RCC) of various stages. In this review, we propose a systematic approach to the assessment of the quantity of the existing predictive and prognostic models.


RESULTS
Only one model was designed to distinguish between malignant or benign histology prior to nephrectomy and another tool attempts to discriminate between low-grade and high-grade histology. Four tools predict the natural history of RCC using preoperative tumor characteristics. Postnephrectomy recurrence can be predicted with four tools. Finally, mortality predictions can be quantified with 21 predictive tools. Although several of these tools are validated, formal tests were performed in surprisingly few such models.


CONCLUSIONS
Multiple models can be applied to nephrectomy candidates, to patients treated with nephrectomy, or to individuals with metastatic RCC regardless of nephrectomy status. For newly diagnosed and untreated patients, these tools can guide the clinician with respect to treatment selection. For patients treated with nephrectomy, they can assess the risk of recurrence and/or mortality and can guide the type and frequency of follow-up considerations. Finally, for patients with metastatic RCC, the models can provide the best estimate of remaining life expectancy. Unfortunately, virtually no data are available to model the prognosis of patients subjected to surveillance or nonextirpative treatment models.
Isbarn
Hendrik
H
Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, Canada.
Karakiewicz
Pierre I
PI