A Multifactorial Postoperative Surveillance Model for Patients With Surgically Treated Clear Cell Renal Cell Carcinoma

Publisher Website
Google Scholar
Abstract
We designed scoring algorithms for postoperative surveillance based on multivariately significant predictors of site specific disease recurrence. We identified 1,864 patients who underwent partial or radical nephrectomy for nonmetastatic clear cell renal cell carcinoma between 1970 and 2000. Clinical features studied included age, sex and symptomatic disease at presentation. Surgical and pathological features studied included nephrectomy type, surgical margin status, 2003 TNM stage, nuclear grade, histological tumor necrosis, sarcomatoid component, cystic architecture and multifocality. Recurrence was classified into locations of abdomen, thoracic region, bone and brain. Recurrence-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were fit to test associations with recurrence. Recurrence to abdomen, the thoracic region, bone and brain developed in 185 (10%), 300 (16%), 134 (7%) and 81 (4%) patients, respectively. Positive surgical margins, 2003 TNM stage, size, grade and necrosis were significantly associated with abdominal recurrence in a multivariate setting. These same features, except surgical margins, were significantly associated with thoracic recurrence. The 2003 TNM stage, grade and necrosis were multivariately predictive of recurrence in bone. Scoring algorithms to predict the likelihood of disease recurrence to these sites and to guide the intensity of postoperative surveillance were developed using regression coefficients from the multivariate models. The proposed scoring algorithms resulted in excellent patient stratification. We present scoring algorithms based on multivariately significant predictors of site specific recurrence that can be used to tailor postoperative surveillance to the individual patient.