3D printed renal cancer models derived from MRI data: application in pre-surgical planning

To determine whether patient-specific 3D printed renal tumor models change pre-operative planning decisions made by urological surgeons in preparation for complex renal mass surgical procedures.

From our ongoing IRB approved study on renal neoplasms, ten renal mass cases were retrospectively selected based on Nephrometry Score greater than 5 (range 6-10). A 3D post-contrast fat-suppressed gradient-echo T1-weighted sequence was used to generate 3D printed models. The cases were evaluated by three experienced urologic oncology surgeons in a randomized fashion using (1) imaging data on PACS alone and (2) 3D printed model in addition to the imaging data. A questionnaire regarding surgical approach and planning was administered. The presumed pre-operative approaches with and without the model were compared. Any change between the presumed approaches and the actual surgical intervention was recorded.

There was a change in planned approach with the 3D printed model for all ten cases with the largest impact seen regarding decisions on transperitoneal or retroperitoneal approach and clamping, with changes seen in 30%-50% of cases. Mean parenchymal volume loss for the operated kidney was 21.4%. Volume losses >20% were associated with increased ischemia times and surgeons tended to report a different approach with the use of the 3D model compared to that with imaging alone in these cases. The 3D printed models helped increase confidence regarding the chosen operative procedure in all cases.

Pre-operative physical 3D models created from MRI data may influence surgical planning for complex kidney cancer.

Abdominal radiology (New York). 2017 Jan 07 [Epub ahead of print]

Nicole Wake, Temitope Rude, Stella K Kang, Michael D Stifelman, James F Borin, Daniel K Sodickson, William C Huang, Hersh Chandarana

Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA. ., Division of Urologic Oncology, Department of Urology, New York University School of Medicine, New York, NY, USA., Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA., Department of Urology, Hackensack University Medical Center, Hackensack, NJ, USA.

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