Multi–Detector Row CT in Evaluation of 94 Living Renal Donors by Readers with Varied Experience

To retrospectively assess the accuracy of four-section multi-detector row computed tomography (CT) in the evaluation of renal transplant donors when scans are read by one of multiple readers with varied levels of expertise, by using surgery as the reference standard.This retrospective study was approved by the institutional review board and complied with the Health Insurance Portability and Accountability Act. Informed consent was waived. Between October 1999 and March 2003, 94 renal donors (42 men, 52 women; mean age, 44 years) underwent four-section multi-detector row CT. Unenhanced scanning of the abdomen was performed with 5-mm section thickness and table speed of 15 mm per rotation. Next, 135-150 mL of nonionic iodinated (300 mg/mL) contrast material was injected intravenously at a rate of 4-5 mL/sec. Contrast material-enhanced CT was initiated 20-25 seconds, 65-70 seconds, and 10 minutes after start of injection. Arterial phase scanning was performed with 1.25-mm section thickness and 7.5-mm table speed. Venous and excretory phase scanning was performed with 2.5-mm section thickness and 15-mm table speed. Each scan was evaluated independently by one of 11 readers for renal vascular and ureteral anatomic variants. Findings at CT were compared with those at surgery. Sensitivity and specificity (with 95% confidence intervals) and accuracy of CT were calculated on the basis of presence or absence of variant anatomy at surgery.CT depicted 107 of 114 renal arteries confirmed at surgery; seven accessory arteries were missed in six donor kidneys. CT depicted 95 of 98 renal veins confirmed at surgery. Sensitivity and specificity of CT were 66% and 100%, 75% and 100%, and 50% and 100%, and overall accuracy was 94%, 97%, and 99%, for identification of variant anatomy of renal arteries, veins, and ureters, respectively.Multi-detector row CT as the sole imaging technique in the preoperative evaluation of living renal donors is accurate even when images are read by multiple readers with varied levels of expertise.