A systematic review of clinical value of three-dimensional printing in renal disease
The aim of this systematic review is to analyse current literature related to the clinical value of three-dimensional (3D) printed models in renal disease. A literature search of PubMed and Scopus databases was performed to identify studies reporting the clinical application and usefulness of 3D printed models in renal disease. Fifteen studies were found to meet the selection criteria and were included in the analysis. Eight of them provided quantitative assessments with five studies focusing on dimensional accuracy of 3D printed models in replicating renal anatomy and tumour, and on measuring tumour volume between 3D printed models and original source images and surgical specimens, with mean difference less than 10%. The other three studies reported that the use of 3D printed models significantly enhanced medical students and specialists’ ability to identify anatomical structures when compared to two-dimensional (2D) images alone; and significantly shortened intraoperative ultrasound duration compared to without use of 3D printed models. Seven studies provided qualitative assessments of the usefulness of 3D printed kidney models with findings showing that 3D printed models improved patient’s understanding of renal anatomy and pathology; improved medical trainees’ understanding of renal malignant tumours when compared to viewing medical images alone; and assisted surgical planning and simulation of renal surgical procedures with significant reductions of intraoperative complications. The cost and time associated with 3D printed kidney model production was reported in 10 studies, with costs ranging from USD$100 to USD$1,000, and duration of 3D printing production up to 31 h. The entire process of 3D printing could take up to a few days. This review shows that 3D printed kidney models are accurate in delineating renal anatomical structures and renal tumours with high accuracy. Patient-specific 3D printed models serve as a useful tool in preoperative planning and simulation of surgical procedures for treatment of renal tumours. Further studies with inclusion of more cases and with a focus on reducing the cost and 3D model production time deserve to be investigated.