Renal tumor contact surface area: A novel parameter for predicting complexity and outcomes of partial nephrectomy

Results and limitations: The mean tumor size was 3.1 cm; CSA was 18.3 cm2. CSA ≥20 cm2 correlated with adverse tumor characteristics (greater tumor size, volume, and complexity) and perioperative outcomes (more parenchymal volume loss, blood loss, and complications) compared with CSA <20 cm2. On multivariable logistic regression, CSA independently predicted operative time, complications, hospital stay, and renal functional outcomes. This predictive ability of CSA was superior to the other parameters evaluated.

Conclusions: CSA is a novel imaging parameter that quantifies the CSA of renal tumor with adjacent parenchyma. Our preliminary data indicate that CSA correlates with PN outcomes. If validated externally in a larger cohort, CSA could be incorporated into future versions of nephrometry scoring systems.

Patient summary: In this study we outline the method of calculating the contact surface area (CSA) of renal tumors with the surrounding normal kidney using image-rendering software. We found that CSA correlates with a number of important surgical outcomes including operative time, loss of renal function, and complications.

Background The contact surface area (CSA) of a tumor with adjacent renal parenchyma may determine the complexity and thus the perioperative outcomes of partial nephrectomy (PN).

Objective: We devised a novel imaging parameter, renal tumor CSA, and correlate it with perioperative outcomes in patients undergoing PN.

Design, setting, and participants: Of 200 patients undergoing PN for a tumor (January 2010 to August 2011), 162 had renal protocol computed tomography scanning data available. CSA was calculated using image-rendering software (Synapse 3D, Fujifilm), and interobserver variability was determined between three independent observers.

Outcome measurements and statistical analysis: CSA was correlated to baseline demographics and perioperative outcomes as a continuous and categorical variable using multivariable logistic regression analysis. The ability of CSA to predict adverse perioperative events was compared with demographic factors and nephrometry scoring systems.