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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

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

BACKGROUND: Patient-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. METHODS: Patients 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. RESULTS: All 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). CONCLUSIONS: All 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. More... »

PAGES

4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s41205-019-0041-3

DOI

http://dx.doi.org/10.1186/s41205-019-0041-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112223653

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30783869


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Center 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, 10016, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wake", 
        "givenName": "Nicole", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Center 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, 10016, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rosenkrantz", 
        "givenName": "Andrew B.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huang", 
        "givenName": "Richard", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Center 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, 10016, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Park", 
        "givenName": "Katalina U.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wysock", 
        "givenName": "James S.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Taneja", 
        "givenName": "Samir S.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huang", 
        "givenName": "William C.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Center 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, 10016, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sodickson", 
        "givenName": "Daniel K.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "New York University", 
          "id": "https://www.grid.ac/institutes/grid.137628.9", 
          "name": [
            "Center 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, 10016, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chandarana", 
        "givenName": "Hersh", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00261-016-1022-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002323786", 
          "https://doi.org/10.1007/s00261-016-1022-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2014.03.042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003422180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2011.04.035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018619410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00345-015-1632-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020621360", 
          "https://doi.org/10.1007/s00345-015-1632-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrurol.2015.242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023275534", 
          "https://doi.org/10.1038/nrurol.2015.242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmjopen-2014-007165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026978041"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eururo.2015.09.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033055850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00002820-200202000-00009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047619112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.crad.2016.02.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047721053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.14.12502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069303645"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00246-017-1586-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083848477", 
          "https://doi.org/10.1007/s00246-017-1586-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00246-017-1586-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083848477", 
          "https://doi.org/10.1007/s00246-017-1586-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/jfb8020013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084652785"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00345-017-2126-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092613782", 
          "https://doi.org/10.1007/s00345-017-2126-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0300060518755267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101007827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0300060518755267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101007827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2017.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101503527"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2017.12.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104142389"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.urology.2017.12.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104142389"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.wneu.2018.05.190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104369886"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-018-1710-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105881824", 
          "https://doi.org/10.1007/s00261-018-1710-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-018-1710-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105881824", 
          "https://doi.org/10.1007/s00261-018-1710-1"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "BACKGROUND: Patient-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.\nMETHODS: Patients 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\u00a0=\u2009200). 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.\nRESULTS: All 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\u00a0<\u20090.05).\nCONCLUSIONS: All 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.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s41205-019-0041-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3858730", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1158738", 
        "issn": [
          "2365-6271"
        ], 
        "name": "3D Printing in Medicine", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "5"
      }
    ], 
    "name": "Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education", 
    "pagination": "4", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s41205-019-0041-3"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3a800985aff3e1509a128c7bf2d1d7e490d8d2ac2cf1739402bf67ee6560ae59"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112223653"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101721758"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30783869"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s41205-019-0041-3", 
      "https://app.dimensions.ai/details/publication/pub.1112223653"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-15T09:01", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000375_0000000375/records_91447_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs41205-019-0041-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s41205-019-0041-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s41205-019-0041-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s41205-019-0041-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s41205-019-0041-3'


 

This table displays all metadata directly associated to this object as RDF triples.

178 TRIPLES      21 PREDICATES      47 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s41205-019-0041-3 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N4e527bd978814847b0876823c9304af3
4 schema:citation sg:pub.10.1007/s00246-017-1586-9
5 sg:pub.10.1007/s00261-016-1022-2
6 sg:pub.10.1007/s00261-018-1710-1
7 sg:pub.10.1007/s00345-015-1632-2
8 sg:pub.10.1007/s00345-017-2126-1
9 sg:pub.10.1038/nrurol.2015.242
10 https://doi.org/10.1016/j.crad.2016.02.012
11 https://doi.org/10.1016/j.eururo.2015.09.024
12 https://doi.org/10.1016/j.urology.2011.04.035
13 https://doi.org/10.1016/j.urology.2014.03.042
14 https://doi.org/10.1016/j.urology.2017.12.001
15 https://doi.org/10.1016/j.urology.2017.12.038
16 https://doi.org/10.1016/j.wneu.2018.05.190
17 https://doi.org/10.1097/00002820-200202000-00009
18 https://doi.org/10.1136/bmjopen-2014-007165
19 https://doi.org/10.1177/0300060518755267
20 https://doi.org/10.2214/ajr.14.12502
21 https://doi.org/10.3390/jfb8020013
22 schema:datePublished 2019-12
23 schema:datePublishedReg 2019-12-01
24 schema:description BACKGROUND: Patient-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. METHODS: Patients 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. RESULTS: All 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). CONCLUSIONS: All 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.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N1b035df8764540a18ac78c9a31546763
29 N83ecc8645cf645a898c3c79942ee146a
30 sg:journal.1158738
31 schema:name Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education
32 schema:pagination 4
33 schema:productId N08d55221a5104019b6756267b3ff0a81
34 N50a64c6d468243378071ead3aa3527fd
35 N8724abefd00a444592f8f3e96032137d
36 Na4533613c6404e5ea77b3711aa7032ef
37 Nce79fb00453d4b608ea2e972c9d5b587
38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112223653
39 https://doi.org/10.1186/s41205-019-0041-3
40 schema:sdDatePublished 2019-04-15T09:01
41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
42 schema:sdPublisher Nb6e100b65c714132a2091b057154e55f
43 schema:url https://link.springer.com/10.1186%2Fs41205-019-0041-3
44 sgo:license sg:explorer/license/
45 sgo:sdDataset articles
46 rdf:type schema:ScholarlyArticle
47 N08d55221a5104019b6756267b3ff0a81 schema:name readcube_id
48 schema:value 3a800985aff3e1509a128c7bf2d1d7e490d8d2ac2cf1739402bf67ee6560ae59
49 rdf:type schema:PropertyValue
50 N1987c5da9134455dab88b10c9daa397f schema:affiliation https://www.grid.ac/institutes/grid.137628.9
51 schema:familyName Wake
52 schema:givenName Nicole
53 rdf:type schema:Person
54 N1b035df8764540a18ac78c9a31546763 schema:volumeNumber 5
55 rdf:type schema:PublicationVolume
56 N1ff4044f81014aee806ce47290bf267b rdf:first N249cb9a2c92d46b082e6116cb817abda
57 rdf:rest rdf:nil
58 N249cb9a2c92d46b082e6116cb817abda schema:affiliation https://www.grid.ac/institutes/grid.137628.9
59 schema:familyName Chandarana
60 schema:givenName Hersh
61 rdf:type schema:Person
62 N40a44387a1d74e3da674f5aa37569f59 rdf:first Nf3d3b9b14abb4552980e5e523916a286
63 rdf:rest N1ff4044f81014aee806ce47290bf267b
64 N4e527bd978814847b0876823c9304af3 rdf:first N1987c5da9134455dab88b10c9daa397f
65 rdf:rest N877ecd97c28f432aaef6a71d6965c2da
66 N4ed5c3c23533485eae38c14a14b1286e rdf:first Nb8abe3f68ed04dfca3a4623f2d4c0baa
67 rdf:rest N97522e0b646f45e19c5cc20601c5f998
68 N4fbce54c75e744a0be227c0620e4384b schema:affiliation https://www.grid.ac/institutes/grid.137628.9
69 schema:familyName Wysock
70 schema:givenName James S.
71 rdf:type schema:Person
72 N50a64c6d468243378071ead3aa3527fd schema:name pubmed_id
73 schema:value 30783869
74 rdf:type schema:PropertyValue
75 N5a0429ac9faa484fb7d8cf3dd872c6f6 schema:affiliation https://www.grid.ac/institutes/grid.137628.9
76 schema:familyName Huang
77 schema:givenName Richard
78 rdf:type schema:Person
79 N6b0959b589e94087847c2707081333d8 schema:affiliation https://www.grid.ac/institutes/grid.137628.9
80 schema:familyName Rosenkrantz
81 schema:givenName Andrew B.
82 rdf:type schema:Person
83 N6f0f67a38caf40ce86d0c9341f08f004 rdf:first N5a0429ac9faa484fb7d8cf3dd872c6f6
84 rdf:rest N4ed5c3c23533485eae38c14a14b1286e
85 N83ecc8645cf645a898c3c79942ee146a schema:issueNumber 1
86 rdf:type schema:PublicationIssue
87 N8724abefd00a444592f8f3e96032137d schema:name dimensions_id
88 schema:value pub.1112223653
89 rdf:type schema:PropertyValue
90 N877ecd97c28f432aaef6a71d6965c2da rdf:first N6b0959b589e94087847c2707081333d8
91 rdf:rest N6f0f67a38caf40ce86d0c9341f08f004
92 N97522e0b646f45e19c5cc20601c5f998 rdf:first N4fbce54c75e744a0be227c0620e4384b
93 rdf:rest Nae09ddf3297a415795cc0e47899fba60
94 Na1a9b23b6de04563b24ec61e50cf2bf7 schema:affiliation https://www.grid.ac/institutes/grid.137628.9
95 schema:familyName Huang
96 schema:givenName William C.
97 rdf:type schema:Person
98 Na4533613c6404e5ea77b3711aa7032ef schema:name doi
99 schema:value 10.1186/s41205-019-0041-3
100 rdf:type schema:PropertyValue
101 Nae09ddf3297a415795cc0e47899fba60 rdf:first Ne26121746624433a94cdf1a4b0f9b1fa
102 rdf:rest Nf6264366c2f44d009a036d04d41523b4
103 Nb6e100b65c714132a2091b057154e55f schema:name Springer Nature - SN SciGraph project
104 rdf:type schema:Organization
105 Nb8abe3f68ed04dfca3a4623f2d4c0baa schema:affiliation https://www.grid.ac/institutes/grid.137628.9
106 schema:familyName Park
107 schema:givenName Katalina U.
108 rdf:type schema:Person
109 Nce79fb00453d4b608ea2e972c9d5b587 schema:name nlm_unique_id
110 schema:value 101721758
111 rdf:type schema:PropertyValue
112 Ne26121746624433a94cdf1a4b0f9b1fa schema:affiliation https://www.grid.ac/institutes/grid.137628.9
113 schema:familyName Taneja
114 schema:givenName Samir S.
115 rdf:type schema:Person
116 Nf3d3b9b14abb4552980e5e523916a286 schema:affiliation https://www.grid.ac/institutes/grid.137628.9
117 schema:familyName Sodickson
118 schema:givenName Daniel K.
119 rdf:type schema:Person
120 Nf6264366c2f44d009a036d04d41523b4 rdf:first Na1a9b23b6de04563b24ec61e50cf2bf7
121 rdf:rest N40a44387a1d74e3da674f5aa37569f59
122 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
123 schema:name Medical and Health Sciences
124 rdf:type schema:DefinedTerm
125 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
126 schema:name Clinical Sciences
127 rdf:type schema:DefinedTerm
128 sg:grant.3858730 http://pending.schema.org/fundedItem sg:pub.10.1186/s41205-019-0041-3
129 rdf:type schema:MonetaryGrant
130 sg:journal.1158738 schema:issn 2365-6271
131 schema:name 3D Printing in Medicine
132 rdf:type schema:Periodical
133 sg:pub.10.1007/s00246-017-1586-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083848477
134 https://doi.org/10.1007/s00246-017-1586-9
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s00261-016-1022-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002323786
137 https://doi.org/10.1007/s00261-016-1022-2
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s00261-018-1710-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105881824
140 https://doi.org/10.1007/s00261-018-1710-1
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s00345-015-1632-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020621360
143 https://doi.org/10.1007/s00345-015-1632-2
144 rdf:type schema:CreativeWork
145 sg:pub.10.1007/s00345-017-2126-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092613782
146 https://doi.org/10.1007/s00345-017-2126-1
147 rdf:type schema:CreativeWork
148 sg:pub.10.1038/nrurol.2015.242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023275534
149 https://doi.org/10.1038/nrurol.2015.242
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.crad.2016.02.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047721053
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.eururo.2015.09.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033055850
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.urology.2011.04.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018619410
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.urology.2014.03.042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003422180
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.urology.2017.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101503527
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.urology.2017.12.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104142389
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.wneu.2018.05.190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104369886
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1097/00002820-200202000-00009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047619112
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1136/bmjopen-2014-007165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026978041
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1177/0300060518755267 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101007827
170 rdf:type schema:CreativeWork
171 https://doi.org/10.2214/ajr.14.12502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069303645
172 rdf:type schema:CreativeWork
173 https://doi.org/10.3390/jfb8020013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084652785
174 rdf:type schema:CreativeWork
175 https://www.grid.ac/institutes/grid.137628.9 schema:alternateName New York University
176 schema:name Center 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, 10016, New York, NY, USA
177 Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU School of Medicine, New York, NY, USA
178 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...