Abdominal Radiology

, Volume 42, Issue 5, pp 1501–1509 | Cite as

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

  • Nicole WakeEmail author
  • Temitope Rude
  • Stella K. Kang
  • Michael D. Stifelman
  • James F. Borin
  • Daniel K. Sodickson
  • William C. Huang
  • Hersh Chandarana



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.

Materials and methods

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.


Partial nephrectomy 3D printing Surgical planning Magnetic resonance imaging Urological oncology 



Magnetic resonance imaging


Computed tomography






Radical nephrectomy




Picture archiving and communication system


Computer-aided design


Region of interest


Compliance with ethical standards


This work was supported by the Center for Advanced Imaging Innovation and Research (, an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Nicole Wake
    • 1
    • 2
    Email author
  • Temitope Rude
    • 3
  • Stella K. Kang
    • 1
    • 4
  • Michael D. Stifelman
    • 5
  • James F. Borin
    • 3
  • Daniel K. Sodickson
    • 1
    • 2
  • William C. Huang
    • 3
  • Hersh Chandarana
    • 1
    • 2
  1. 1.Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical ImagingNew York University School of MedicineNew YorkUSA
  2. 2.Sackler Institute of Graduate Biomedical SciencesNew York University School of MedicineNew YorkUSA
  3. 3.Division of Urologic Oncology, Department of UrologyNew York University School of MedicineNew YorkUSA
  4. 4.Department of Population HealthNew York University School of MedicineNew YorkUSA
  5. 5.Department of UrologyHackensack University Medical CenterHackensackUSA

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