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

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Abstract

Objective

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.

Results

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.

Conclusions

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

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Abbreviations

MRI:

Magnetic resonance imaging

CT:

Computed tomography

3D:

Three-dimensional

2D:

Two-dimensional

RN:

Radical nephrectomy

STL:

Stereolithography

PACS:

Picture archiving and communication system

CAD:

Computer-aided design

ROI:

Region of interest

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Authors and Affiliations

  1. 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

    Nicole Wake, Stella K. Kang, Daniel K. Sodickson & Hersh Chandarana

  2. Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA

    Nicole Wake, Daniel K. Sodickson & Hersh Chandarana

  3. Division of Urologic Oncology, Department of Urology, New York University School of Medicine, New York, NY, USA

    Temitope Rude, James F. Borin & William C. Huang

  4. Department of Population Health, New York University School of Medicine, New York, NY, USA

    Stella K. Kang

  5. Department of Urology, Hackensack University Medical Center, Hackensack, NJ, USA

    Michael D. Stifelman

Authors
  1. Nicole Wake
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  2. Temitope Rude
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  3. Stella K. Kang
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  4. Michael D. Stifelman
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  5. James F. Borin
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  6. Daniel K. Sodickson
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  7. William C. Huang
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  8. Hersh Chandarana
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Corresponding author

Correspondence to Nicole Wake.

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Funding

This work was supported by the Center for Advanced Imaging Innovation and Research (www.cai2r.net), 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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Wake, N., Rude, T., Kang, S.K. et al. 3D printed renal cancer models derived from MRI data: application in pre-surgical planning. Abdom Radiol 42, 1501–1509 (2017). https://doi.org/10.1007/s00261-016-1022-2

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  • DOI: https://doi.org/10.1007/s00261-016-1022-2

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