Evidence map›Paper›PMID 30783869›Full record

Article3D printing in medicine2019

Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education.

Nicole Wake, Andrew B Rosenkrantz, Richard Huang, Katalina U Park, James S Wysock, Samir S Taneja, William C Huang, Daniel K Sodickson, Hersh Chandarana

Abstract read
In one paragraph

Article in 3D printing in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

100 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Trial
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

40 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nicole WakeCenter for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU School of Medicine, 660 First Avenue, Fourth Floor, New York, NY, 10016, USA. nicole.wake@nyulangone.org.ORCID http://orcid.org/0000-0002-8441-6059
Andrew B RosenkrantzCenter for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU School of Medicine, 660 First Avenue, Fourth Floor, New York, NY, 10016, USA.
Richard HuangDivision of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA.
Katalina U ParkCenter for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU School of Medicine, 660 First Avenue, Fourth Floor, New York, NY, 10016, USA.
James S WysockDivision of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA.
Samir S TanejaDivision of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA.
William C HuangDivision of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA.
Daniel K SodicksonCenter for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU School of Medicine, 660 First Avenue, Fourth Floor, New York, NY, 10016, USA.
Hersh ChandaranaCenter for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU School of Medicine, 660 First Avenue, Fourth Floor, New York, NY, 10016, USA.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
National Institute of Biomedical Imaging and Bioengineering NIH P41 EB017183NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

backgroundPatient-specific 3D models are being used increasingly in medicine for many applications including surgical planning, procedure rehearsal, trainee education, and patient education. To date, experiences on the use of 3D models to facilitate patient understanding of their disease and surgical plan are limited. The purpose of this study was to investigate in the context of renal and prostate cancer the impact of using 3D printed and augmented reality models for patient education.

methodsPatients with MRI-visible prostate cancer undergoing either robotic assisted radical prostatectomy or focal ablative therapy or patients with renal masses undergoing partial nephrectomy were prospectively enrolled in this IRB approved study (n = 200). Patients underwent routine clinical imaging protocols and were randomized to receive pre-operative planning with imaging alone or imaging plus a patient-specific 3D model which was either 3D printed, visualized in AR, or viewed in 3D on a 2D computer monitor. 3D uro-oncologic models were created from the medical imaging data. A 5-point Likert scale survey was administered to patients prior to the surgical procedure to determine understanding of the cancer and treatment plan. If randomized to receive a pre-operative 3D model, the survey was completed twice, before and after viewing the 3D model. In addition, the cohort that received 3D models completed additional questions to compare usefulness of the different forms of visualization of the 3D models. Survey responses for each of the 3D model groups were compared using the Mann-Whitney and Wilcoxan rank-sum tests.

resultsAll 200 patients completed the survey after reviewing their cases with their surgeons using imaging only. 127 patients completed the 5-point Likert scale survey regarding understanding of disease and surgical procedure twice, once with imaging and again after reviewing imaging plus a 3D model. Patients had a greater understanding using 3D printed models versus imaging for all measures including comprehension of disease, cancer size, cancer location, treatment plan, and the comfort level regarding the treatment plan (range 4.60-4.78/5 vs. 4.06-4.49/5, p < 0.05).

conclusionsAll types of patient-specific 3D models were reported to be valuable for patient education. Out of the three advanced imaging methods, the 3D printed models helped patients to have the greatest understanding of their anatomy, disease, tumor characteristics, and surgical procedure.

Indexed as

3D printingAugmented realityKidney cancerProstate cancerUrologic oncology

Identifiers

PMID30783869
PMCPMC6743040

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.