Evaluation of diagnostic accuracy: multidetector CT image noise correction improves specificity of a Gaussian model-based algorithm used for characterization of ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Toshimasa J. Clark, Larson D. Hsu, Daniel Hippe, Sophie Cowan, Jonathan Carnell, Carolyn L. Wang

ABSTRACT

OBJECTIVES: To investigate whether the histogram analysis method of characterizing adrenal nodules as adenomas is affected by increased noise with modern CT technique, and if an extension that allows for noise correction will improve diagnostic performance. MATERIALS AND METHODS: This is a HIPAA-compliant, IRB-approved retrospective study performed on 58 total patients. The first group of 29 patients had 33 adrenal lesions that were pathology-proven non-adenomas. The second group had 29 patients with 33 pathology-proven or presumed adenomas based on established imaging criteria. The nodules were evaluated using the histogram method, mean attenuation method, and a Gaussian model-based algorithm without (uncorrected Gaussian algorithm) and with correction (corrected Gaussian algorithm) for image noise. Sensitivity, specificity, and accuracy for identifying adenoma were derived. RESULTS: There were no significant differences in identifying adenoma from non-adenoma when using the histogram analysis method and the uncorrected Gaussian algorithm, both of which had low specificities of 42.4% and 47.0%, respectively (p = 0.30). Adding noise correction to the Gaussian algorithm resulted in a statistically significant increase in specificity relative to the histogram method (86.4% vs. 42.4%, p < 0.001). The corrected Gaussian algorithm improved sensitivity compared to the mean attenuation method (71.2% vs. 54.5%, p < 0.001), but had lower specificity (86.4% vs. 100%, p < 0.001), and similar overall accuracy (78.8% vs. 77.3%, p = 0.74). CONCLUSION: With modern low-dose CT technique, the specificity scores of the histogram method for discrimination of adrenal adenomas and non-adenomas are lower than with previous higher dose scans. The specificity and accuracy of a histogram-equivalent method can be increased mathematically through image noise correction, and the corrected Gaussian algorithm has improved sensitivity to the mean attenuation with similar accuracy albeit with lower specificity. Although this suggests limited utility for histogram analysis in adrenal nodule characterization, our study demonstrates the potential mathematical application for other noise-dependent CT characterization methods. More... »

PAGES

1033-1043

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00261-018-1871-y

DOI

http://dx.doi.org/10.1007/s00261-018-1871-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111045174

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30600378


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": "University of Colorado Anschutz Medical Campus", 
          "id": "https://www.grid.ac/institutes/grid.430503.1", 
          "name": [
            "Abdominal Imaging Division, Department of Radiology, University of Colorado Denver, Anschutz Medical Campus, 12401 E 17th Ave, Mail Stop L954, 80045, Aurora, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Clark", 
        "givenName": "Toshimasa J.", 
        "id": "sg:person.0672551072.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0672551072.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Roswell Park Cancer Institute", 
          "id": "https://www.grid.ac/institutes/grid.240614.5", 
          "name": [
            "Department of Radiology, Roswell Park Cancer Institute, Body Imaging Section Elm & Carlton Streets, 14263, Buffalo, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsu", 
        "givenName": "Larson D.", 
        "id": "sg:person.010514215425.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010514215425.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hippe", 
        "givenName": "Daniel", 
        "id": "sg:person.0764104440.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764104440.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cowan", 
        "givenName": "Sophie", 
        "id": "sg:person.013263430224.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013263430224.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Carnell", 
        "givenName": "Jonathan", 
        "id": "sg:person.013724206003.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013724206003.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Carolyn L.", 
        "id": "sg:person.01176525467.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176525467.80"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.ejrad.2008.12.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013002764"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2223010766", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032061057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2493070976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037914491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2283020878", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043311254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1067/j.cpradiol.2016.02.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047530692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-014-0307-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049448562", 
          "https://doi.org/10.1007/s00261-014-0307-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.05.0179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069297458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.05.1022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069297759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.07.2799", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069298839"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.07.3150", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069299021"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.10.5342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069301201"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.15.15451", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069304519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4329/wjr.v8.i12.902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072524915"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.179.2.2014283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078047332"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/rg.2017170056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091621070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.17.19159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107521267"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03", 
    "datePublishedReg": "2019-03-01", 
    "description": "OBJECTIVES: To investigate whether the histogram analysis method of characterizing adrenal nodules as adenomas is affected by increased noise with modern CT technique, and if an extension that allows for noise correction will improve diagnostic performance.\nMATERIALS AND METHODS: This is a HIPAA-compliant, IRB-approved retrospective study performed on 58 total patients. The first group of 29 patients had 33 adrenal lesions that were pathology-proven non-adenomas. The second group had 29 patients with 33 pathology-proven or presumed adenomas based on established imaging criteria. The nodules were evaluated using the histogram method, mean attenuation method, and a Gaussian model-based algorithm without (uncorrected Gaussian algorithm) and with correction (corrected Gaussian algorithm) for image noise. Sensitivity, specificity, and accuracy for identifying adenoma were derived.\nRESULTS: There were no significant differences in identifying adenoma from non-adenoma when using the histogram analysis method and the uncorrected Gaussian algorithm, both of which had low specificities of 42.4% and 47.0%, respectively (p\u2009=\u20090.30). Adding noise correction to the Gaussian algorithm resulted in a statistically significant increase in specificity relative to the histogram method (86.4% vs. 42.4%, p\u2009<\u20090.001). The corrected Gaussian algorithm improved sensitivity compared to the mean attenuation method (71.2% vs. 54.5%, p\u2009<\u20090.001), but had lower specificity (86.4% vs. 100%, p\u2009<\u20090.001), and similar overall accuracy (78.8% vs. 77.3%, p\u2009=\u20090.74).\nCONCLUSION: With modern low-dose CT technique, the specificity scores of the histogram method for discrimination of adrenal adenomas and non-adenomas are lower than with previous higher dose scans. The specificity and accuracy of a histogram-equivalent method can be increased mathematically through image noise correction, and the corrected Gaussian algorithm has improved sensitivity to the mean attenuation with similar accuracy albeit with lower specificity. Although this suggests limited utility for histogram analysis in adrenal nodule characterization, our study demonstrates the potential mathematical application for other noise-dependent CT characterization methods.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00261-018-1871-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297457", 
        "issn": [
          "2366-004X", 
          "2366-0058"
        ], 
        "name": "Abdominal Radiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "44"
      }
    ], 
    "name": "Evaluation of diagnostic accuracy: multidetector CT image noise correction improves specificity of a Gaussian model-based algorithm used for characterization of incidental adrenal nodules", 
    "pagination": "1033-1043", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b63b7e6b4de13f7e4dd0d9fdb75af690dd649ed684aa3f7490d15fba20997d7c"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30600378"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101674571"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00261-018-1871-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111045174"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00261-018-1871-y", 
      "https://app.dimensions.ai/details/publication/pub.1111045174"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:10", 
    "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/v90/0000000361_0000000361/records_53977_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00261-018-1871-y"
  }
]
 

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.10.1007/s00261-018-1871-y'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00261-018-1871-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00261-018-1871-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00261-018-1871-y'


 

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

164 TRIPLES      21 PREDICATES      45 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00261-018-1871-y schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N9a286ded4c324b0691738330aa6019d1
4 schema:citation sg:pub.10.1007/s00261-014-0307-6
5 https://doi.org/10.1016/j.ejrad.2008.12.010
6 https://doi.org/10.1067/j.cpradiol.2016.02.011
7 https://doi.org/10.1148/radiol.2223010766
8 https://doi.org/10.1148/radiol.2283020878
9 https://doi.org/10.1148/radiol.2493070976
10 https://doi.org/10.1148/radiology.179.2.2014283
11 https://doi.org/10.1148/rg.2017170056
12 https://doi.org/10.2214/ajr.05.0179
13 https://doi.org/10.2214/ajr.05.1022
14 https://doi.org/10.2214/ajr.07.2799
15 https://doi.org/10.2214/ajr.07.3150
16 https://doi.org/10.2214/ajr.10.5342
17 https://doi.org/10.2214/ajr.15.15451
18 https://doi.org/10.2214/ajr.17.19159
19 https://doi.org/10.4329/wjr.v8.i12.902
20 schema:datePublished 2019-03
21 schema:datePublishedReg 2019-03-01
22 schema:description OBJECTIVES: To investigate whether the histogram analysis method of characterizing adrenal nodules as adenomas is affected by increased noise with modern CT technique, and if an extension that allows for noise correction will improve diagnostic performance. MATERIALS AND METHODS: This is a HIPAA-compliant, IRB-approved retrospective study performed on 58 total patients. The first group of 29 patients had 33 adrenal lesions that were pathology-proven non-adenomas. The second group had 29 patients with 33 pathology-proven or presumed adenomas based on established imaging criteria. The nodules were evaluated using the histogram method, mean attenuation method, and a Gaussian model-based algorithm without (uncorrected Gaussian algorithm) and with correction (corrected Gaussian algorithm) for image noise. Sensitivity, specificity, and accuracy for identifying adenoma were derived. RESULTS: There were no significant differences in identifying adenoma from non-adenoma when using the histogram analysis method and the uncorrected Gaussian algorithm, both of which had low specificities of 42.4% and 47.0%, respectively (p = 0.30). Adding noise correction to the Gaussian algorithm resulted in a statistically significant increase in specificity relative to the histogram method (86.4% vs. 42.4%, p < 0.001). The corrected Gaussian algorithm improved sensitivity compared to the mean attenuation method (71.2% vs. 54.5%, p < 0.001), but had lower specificity (86.4% vs. 100%, p < 0.001), and similar overall accuracy (78.8% vs. 77.3%, p = 0.74). CONCLUSION: With modern low-dose CT technique, the specificity scores of the histogram method for discrimination of adrenal adenomas and non-adenomas are lower than with previous higher dose scans. The specificity and accuracy of a histogram-equivalent method can be increased mathematically through image noise correction, and the corrected Gaussian algorithm has improved sensitivity to the mean attenuation with similar accuracy albeit with lower specificity. Although this suggests limited utility for histogram analysis in adrenal nodule characterization, our study demonstrates the potential mathematical application for other noise-dependent CT characterization methods.
23 schema:genre research_article
24 schema:inLanguage en
25 schema:isAccessibleForFree false
26 schema:isPartOf N35ff20a34b7b4ae5a356d9b4886b1937
27 N9b3ba9e6b6744a8ebc3675ee79e3b39f
28 sg:journal.1297457
29 schema:name Evaluation of diagnostic accuracy: multidetector CT image noise correction improves specificity of a Gaussian model-based algorithm used for characterization of incidental adrenal nodules
30 schema:pagination 1033-1043
31 schema:productId N0ff0d89b23764df3baf37d400d36d2bc
32 N435b039ba6dc42ef96bdba4fb9d2add3
33 N79880b894b7d4a59b242047a4ecd4b4f
34 N7e21177137624e51a1807ff17173b794
35 N7e60e5d9e7ab4c62b9fb6ebe37d4ea88
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111045174
37 https://doi.org/10.1007/s00261-018-1871-y
38 schema:sdDatePublished 2019-04-11T12:10
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher N1808182f399e4b98b04ab8a2dbc741a6
41 schema:url https://link.springer.com/10.1007%2Fs00261-018-1871-y
42 sgo:license sg:explorer/license/
43 sgo:sdDataset articles
44 rdf:type schema:ScholarlyArticle
45 N0ff0d89b23764df3baf37d400d36d2bc schema:name doi
46 schema:value 10.1007/s00261-018-1871-y
47 rdf:type schema:PropertyValue
48 N1808182f399e4b98b04ab8a2dbc741a6 schema:name Springer Nature - SN SciGraph project
49 rdf:type schema:Organization
50 N29b4eb091d714805904c013020a8cd96 schema:name Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA
51 rdf:type schema:Organization
52 N35ff20a34b7b4ae5a356d9b4886b1937 schema:volumeNumber 44
53 rdf:type schema:PublicationVolume
54 N435b039ba6dc42ef96bdba4fb9d2add3 schema:name pubmed_id
55 schema:value 30600378
56 rdf:type schema:PropertyValue
57 N4e98fd6b39384c4f87f01374cb37af08 schema:name Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA
58 rdf:type schema:Organization
59 N79880b894b7d4a59b242047a4ecd4b4f schema:name nlm_unique_id
60 schema:value 101674571
61 rdf:type schema:PropertyValue
62 N7e21177137624e51a1807ff17173b794 schema:name dimensions_id
63 schema:value pub.1111045174
64 rdf:type schema:PropertyValue
65 N7e60e5d9e7ab4c62b9fb6ebe37d4ea88 schema:name readcube_id
66 schema:value b63b7e6b4de13f7e4dd0d9fdb75af690dd649ed684aa3f7490d15fba20997d7c
67 rdf:type schema:PropertyValue
68 N831dfb2e2aa74b55b8c0a3fbbbb48b09 rdf:first sg:person.013263430224.43
69 rdf:rest N89c25088e1004fbe82ac4bce3a2ac91d
70 N89c25088e1004fbe82ac4bce3a2ac91d rdf:first sg:person.013724206003.75
71 rdf:rest Nb9a08fa3f8114ccaaf6ae93f5a1ac73d
72 N9094ba86a03749f2b2b522c98d26a913 schema:name Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA
73 rdf:type schema:Organization
74 N9a286ded4c324b0691738330aa6019d1 rdf:first sg:person.0672551072.70
75 rdf:rest Ned5bbeced6e248889c97402082e006e4
76 N9b3ba9e6b6744a8ebc3675ee79e3b39f schema:issueNumber 3
77 rdf:type schema:PublicationIssue
78 Na9e1b18849f14a8c92ad13f07c1efd4a schema:name Body Imaging Section, Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Box 358081, 98109, Seattle, WA, USA
79 rdf:type schema:Organization
80 Nb9a08fa3f8114ccaaf6ae93f5a1ac73d rdf:first sg:person.01176525467.80
81 rdf:rest rdf:nil
82 Ned5bbeced6e248889c97402082e006e4 rdf:first sg:person.010514215425.93
83 rdf:rest Nf813f3676bee48a4a39a1a11156ecc39
84 Nf813f3676bee48a4a39a1a11156ecc39 rdf:first sg:person.0764104440.34
85 rdf:rest N831dfb2e2aa74b55b8c0a3fbbbb48b09
86 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
87 schema:name Medical and Health Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
90 schema:name Clinical Sciences
91 rdf:type schema:DefinedTerm
92 sg:journal.1297457 schema:issn 2366-004X
93 2366-0058
94 schema:name Abdominal Radiology
95 rdf:type schema:Periodical
96 sg:person.010514215425.93 schema:affiliation https://www.grid.ac/institutes/grid.240614.5
97 schema:familyName Hsu
98 schema:givenName Larson D.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010514215425.93
100 rdf:type schema:Person
101 sg:person.01176525467.80 schema:affiliation Na9e1b18849f14a8c92ad13f07c1efd4a
102 schema:familyName Wang
103 schema:givenName Carolyn L.
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176525467.80
105 rdf:type schema:Person
106 sg:person.013263430224.43 schema:affiliation N4e98fd6b39384c4f87f01374cb37af08
107 schema:familyName Cowan
108 schema:givenName Sophie
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013263430224.43
110 rdf:type schema:Person
111 sg:person.013724206003.75 schema:affiliation N29b4eb091d714805904c013020a8cd96
112 schema:familyName Carnell
113 schema:givenName Jonathan
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013724206003.75
115 rdf:type schema:Person
116 sg:person.0672551072.70 schema:affiliation https://www.grid.ac/institutes/grid.430503.1
117 schema:familyName Clark
118 schema:givenName Toshimasa J.
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0672551072.70
120 rdf:type schema:Person
121 sg:person.0764104440.34 schema:affiliation N9094ba86a03749f2b2b522c98d26a913
122 schema:familyName Hippe
123 schema:givenName Daniel
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764104440.34
125 rdf:type schema:Person
126 sg:pub.10.1007/s00261-014-0307-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049448562
127 https://doi.org/10.1007/s00261-014-0307-6
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.ejrad.2008.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013002764
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1067/j.cpradiol.2016.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047530692
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1148/radiol.2223010766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032061057
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1148/radiol.2283020878 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043311254
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1148/radiol.2493070976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037914491
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1148/radiology.179.2.2014283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078047332
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1148/rg.2017170056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091621070
142 rdf:type schema:CreativeWork
143 https://doi.org/10.2214/ajr.05.0179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069297458
144 rdf:type schema:CreativeWork
145 https://doi.org/10.2214/ajr.05.1022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069297759
146 rdf:type schema:CreativeWork
147 https://doi.org/10.2214/ajr.07.2799 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069298839
148 rdf:type schema:CreativeWork
149 https://doi.org/10.2214/ajr.07.3150 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069299021
150 rdf:type schema:CreativeWork
151 https://doi.org/10.2214/ajr.10.5342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069301201
152 rdf:type schema:CreativeWork
153 https://doi.org/10.2214/ajr.15.15451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069304519
154 rdf:type schema:CreativeWork
155 https://doi.org/10.2214/ajr.17.19159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107521267
156 rdf:type schema:CreativeWork
157 https://doi.org/10.4329/wjr.v8.i12.902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072524915
158 rdf:type schema:CreativeWork
159 https://www.grid.ac/institutes/grid.240614.5 schema:alternateName Roswell Park Cancer Institute
160 schema:name Department of Radiology, Roswell Park Cancer Institute, Body Imaging Section Elm & Carlton Streets, 14263, Buffalo, NY, USA
161 rdf:type schema:Organization
162 https://www.grid.ac/institutes/grid.430503.1 schema:alternateName University of Colorado Anschutz Medical Campus
163 schema:name Abdominal Imaging Division, Department of Radiology, University of Colorado Denver, Anschutz Medical Campus, 12401 E 17th Ave, Mail Stop L954, 80045, Aurora, CO, USA
164 rdf:type schema:Organization
 




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


...