Test–Retest Reproducibility Analysis of Lung CT Image Features View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Yoganand Balagurunathan, Virendra Kumar, Yuhua Gu, Jongphil Kim, Hua Wang, Ying Liu, Dmitry B. Goldgof, Lawrence O. Hall, Rene Korn, Binsheng Zhao, Lawrence H. Schwartz, Satrajit Basu, Steven Eschrich, Robert A. Gatenby, Robert J. Gillies

ABSTRACT

Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC. More... »

PAGES

805-823

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10278-014-9716-x

DOI

http://dx.doi.org/10.1007/s10278-014-9716-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028782764

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24990346


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Area Under Curve", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Image Processing, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lung", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lung Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Balagurunathan", 
        "givenName": "Yoganand", 
        "id": "sg:person.01246004652.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246004652.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kumar", 
        "givenName": "Virendra", 
        "id": "sg:person.0671266353.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0671266353.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gu", 
        "givenName": "Yuhua", 
        "id": "sg:person.01362605550.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01362605550.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Jongphil", 
        "id": "sg:person.01251516646.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251516646.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tianjin Medical University Cancer Institute and Hospital", 
          "id": "https://www.grid.ac/institutes/grid.411918.4", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA", 
            "Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Hua", 
        "id": "sg:person.01300443404.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01300443404.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tianjin Medical University Cancer Institute and Hospital", 
          "id": "https://www.grid.ac/institutes/grid.411918.4", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA", 
            "Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Ying", 
        "id": "sg:person.01324730712.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324730712.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Florida", 
          "id": "https://www.grid.ac/institutes/grid.170693.a", 
          "name": [
            "Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Goldgof", 
        "givenName": "Dmitry B.", 
        "id": "sg:person.01320333450.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320333450.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Florida", 
          "id": "https://www.grid.ac/institutes/grid.170693.a", 
          "name": [
            "Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hall", 
        "givenName": "Lawrence O.", 
        "id": "sg:person.012516676542.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012516676542.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Definiens AG, Bernhard-Wicki-Stra\u00dfe 5, 80636, Munchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korn", 
        "givenName": "Rene", 
        "id": "sg:person.0754227410.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0754227410.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Columbia University", 
          "id": "https://www.grid.ac/institutes/grid.21729.3f", 
          "name": [
            "Department of Radiology, Columbia University, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Binsheng", 
        "id": "sg:person.01267432547.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01267432547.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Columbia University", 
          "id": "https://www.grid.ac/institutes/grid.21729.3f", 
          "name": [
            "Department of Radiology, Columbia University, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schwartz", 
        "givenName": "Lawrence H.", 
        "id": "sg:person.011027760412.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011027760412.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Florida", 
          "id": "https://www.grid.ac/institutes/grid.170693.a", 
          "name": [
            "Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Basu", 
        "givenName": "Satrajit", 
        "id": "sg:person.0637201450.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637201450.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Eschrich", 
        "givenName": "Steven", 
        "id": "sg:person.010462044607.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010462044607.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gatenby", 
        "givenName": "Robert A.", 
        "id": "sg:person.01251663701.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251663701.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Moffitt Cancer Center", 
          "id": "https://www.grid.ac/institutes/grid.468198.a", 
          "name": [
            "Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA", 
            "Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA", 
            "Experimental Imaging Program, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, SRB-2, 33612, Tampa, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gillies", 
        "givenName": "Robert J.", 
        "id": "sg:person.014224135057.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014224135057.83"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.acra.2008.10.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000654335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-71331-9_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001716602", 
          "https://doi.org/10.1007/978-3-540-71331-9_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-200301000-00011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002181792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-200301000-00011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002181792"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.acra.2009.06.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003650188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2011/361589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005187391"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djj403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007567909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3140589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007720499"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2522081593", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008413249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009424564"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2319-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010883759", 
          "https://doi.org/10.1007/s00330-011-2319-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2319-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010883759", 
          "https://doi.org/10.1007/s00330-011-2319-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/rg.282075068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015440619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9868.00346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018285816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1164/rccm.200703-462pp", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023963564"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djq025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031376038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djq025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031376038"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1158/1078-0432.ccr-10-0125", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032189617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10278-009-9185-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036249920", 
          "https://doi.org/10.1007/s10278-009-9185-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10278-009-9185-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036249920", 
          "https://doi.org/10.1007/s10278-009-9185-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2203001701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037493640"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.artmed.2010.04.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038200455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.lungcan.2010.03.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044013433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1102/1470-7330.2010.0021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045447398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048850628", 
          "https://doi.org/10.1038/nbt1306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000068410", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049121680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2012.10.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049617138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(94)90127-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052101887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(94)90127-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052101887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.574797", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156543"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tbme.2008.919735", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061527524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2174/138620709789383196", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069174589"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2529310", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069974986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2532031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069977459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2532051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069977481"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2005.1617159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077368930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icsmc.2011.6083840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094832561"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient\u2009\u2265\u20090.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet\u2009\u2265\u20090.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10278-014-9716-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2689152", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2438867", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1100894", 
        "issn": [
          "0897-1889", 
          "1618-727X"
        ], 
        "name": "Journal of Digital Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "name": "Test\u2013Retest Reproducibility Analysis of Lung CT Image Features", 
    "pagination": "805-823", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "891d20d466d75b22f047eb4184c5a5cac3b983145e688f0aab30ef51fd7921e0"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24990346"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9100529"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10278-014-9716-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1028782764"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10278-014-9716-x", 
      "https://app.dimensions.ai/details/publication/pub.1028782764"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:50", 
    "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/v90/0000000001_0000000264/records_8681_00000481.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10278-014-9716-x"
  }
]
 

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/s10278-014-9716-x'

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/s10278-014-9716-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10278-014-9716-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10278-014-9716-x'


 

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

350 TRIPLES      21 PREDICATES      77 URIs      37 LITERALS      25 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10278-014-9716-x schema:about N02151a43c6aa4e189ff97d258f7fa4d1
2 N14d79014ad76477bbcc55749c2da8c95
3 N17f52874281447548220b898250b9425
4 N2c6e410a872b44d5a4dd39cf271d811f
5 N7dd9df099f204517906cf58a7447de42
6 N91d980d072da480b8e8808ffa1bde384
7 N91e8fd4deccf4fa98d30677baa5e3ac0
8 Na417516292714a6ba668d33971aca1de
9 Nabc0925e773647c5a4aeea42af4bede4
10 Nc863fa274a264200a4c1f7d78059d9dc
11 Nd0a450ceebb2450cb43cf96749ff1d96
12 Nd9a3062df2ce4190af8b442726bbc841
13 Nddb398bf014a4ad09828c709493a9b46
14 Ndf85f8492c6245c286e649756be8dd03
15 Ne936c76a52d046cbae2f03fef744cbbb
16 Ne9af1e921e974e1fa0ac363fb15fabe8
17 anzsrc-for:08
18 anzsrc-for:0801
19 schema:author N6b7a5bb98dd04cdeb0009033283cdf52
20 schema:citation sg:pub.10.1007/978-3-540-71331-9_15
21 sg:pub.10.1007/s00330-011-2319-8
22 sg:pub.10.1007/s10278-009-9185-9
23 sg:pub.10.1038/nbt1306
24 https://doi.org/10.1016/0167-8655(94)90127-9
25 https://doi.org/10.1016/j.acra.2008.10.009
26 https://doi.org/10.1016/j.acra.2009.06.019
27 https://doi.org/10.1016/j.artmed.2010.04.011
28 https://doi.org/10.1016/j.lungcan.2010.03.009
29 https://doi.org/10.1016/j.patcog.2012.10.005
30 https://doi.org/10.1093/bioinformatics/btm344
31 https://doi.org/10.1093/jnci/djj403
32 https://doi.org/10.1093/jnci/djq025
33 https://doi.org/10.1097/00004728-200301000-00011
34 https://doi.org/10.1102/1470-7330.2010.0021
35 https://doi.org/10.1109/34.574797
36 https://doi.org/10.1109/icsmc.2011.6083840
37 https://doi.org/10.1109/iembs.2005.1617159
38 https://doi.org/10.1109/tbme.2008.919735
39 https://doi.org/10.1111/1467-9868.00346
40 https://doi.org/10.1118/1.3140589
41 https://doi.org/10.1148/radiol.2203001701
42 https://doi.org/10.1148/radiol.2522081593
43 https://doi.org/10.1148/rg.282075068
44 https://doi.org/10.1155/2011/361589
45 https://doi.org/10.1158/1078-0432.ccr-10-0125
46 https://doi.org/10.1159/000068410
47 https://doi.org/10.1164/rccm.200703-462pp
48 https://doi.org/10.2174/138620709789383196
49 https://doi.org/10.2307/2529310
50 https://doi.org/10.2307/2532031
51 https://doi.org/10.2307/2532051
52 schema:datePublished 2014-12
53 schema:datePublishedReg 2014-12-01
54 schema:description Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
55 schema:genre research_article
56 schema:inLanguage en
57 schema:isAccessibleForFree true
58 schema:isPartOf Naeb14968ea5749d986e8aee1c195bbc2
59 Ncaf953e1e6a3499db7ab7b2e15ca70f0
60 sg:journal.1100894
61 schema:name Test–Retest Reproducibility Analysis of Lung CT Image Features
62 schema:pagination 805-823
63 schema:productId N09e6fe846a49443798736591d88e5979
64 N1bedae1b237c461884b00184f95e8c8e
65 N9f4e0cd28d8d4997b1f15d47c9603642
66 Nc32666e0a81e42ddbbc55212ea2cc58f
67 Ncde4acec65f7471db1ff5ae6ae2dba29
68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028782764
69 https://doi.org/10.1007/s10278-014-9716-x
70 schema:sdDatePublished 2019-04-10T19:50
71 schema:sdLicense https://scigraph.springernature.com/explorer/license/
72 schema:sdPublisher N76293f90d2124dc491a3c26307de8c6e
73 schema:url http://link.springer.com/10.1007/s10278-014-9716-x
74 sgo:license sg:explorer/license/
75 sgo:sdDataset articles
76 rdf:type schema:ScholarlyArticle
77 N02151a43c6aa4e189ff97d258f7fa4d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Humans
79 rdf:type schema:DefinedTerm
80 N09e6fe846a49443798736591d88e5979 schema:name nlm_unique_id
81 schema:value 9100529
82 rdf:type schema:PropertyValue
83 N1123c0c4d73047d2a97b7ce201636101 rdf:first sg:person.014224135057.83
84 rdf:rest rdf:nil
85 N141c702d0d0c4320b33117bf29c36b77 rdf:first sg:person.0754227410.45
86 rdf:rest N48a45a36391d450b8b2cc4a58cc1ee60
87 N14d79014ad76477bbcc55749c2da8c95 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Area Under Curve
89 rdf:type schema:DefinedTerm
90 N17f52874281447548220b898250b9425 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Lung Neoplasms
92 rdf:type schema:DefinedTerm
93 N186e9f6f04304846b0acb5f10379426d rdf:first sg:person.012516676542.25
94 rdf:rest N141c702d0d0c4320b33117bf29c36b77
95 N1bedae1b237c461884b00184f95e8c8e schema:name doi
96 schema:value 10.1007/s10278-014-9716-x
97 rdf:type schema:PropertyValue
98 N1f52234fa22a459aac036d5451261925 rdf:first sg:person.01324730712.78
99 rdf:rest Nf853fb45c80842e0bc725d672bc030b1
100 N2027198e54f749288877e466c56f27dc rdf:first sg:person.01362605550.26
101 rdf:rest Na04b12baf263477db0c1a3e59f7376bc
102 N2c6e410a872b44d5a4dd39cf271d811f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Adult
104 rdf:type schema:DefinedTerm
105 N48a45a36391d450b8b2cc4a58cc1ee60 rdf:first sg:person.01267432547.98
106 rdf:rest Nc7060ee2a453454892cad42f71bf4b80
107 N4e4b79c8e2da43fa848f574d38ad0a66 rdf:first sg:person.01300443404.06
108 rdf:rest N1f52234fa22a459aac036d5451261925
109 N512bde95ef504708ac4c381066bcd292 rdf:first sg:person.01251663701.28
110 rdf:rest N1123c0c4d73047d2a97b7ce201636101
111 N67668d5e3c8f45d8a9951a44a3e5bc04 rdf:first sg:person.010462044607.31
112 rdf:rest N512bde95ef504708ac4c381066bcd292
113 N6b7a5bb98dd04cdeb0009033283cdf52 rdf:first sg:person.01246004652.76
114 rdf:rest Nd03155d4a15f417fa8b87fc356443227
115 N6e640894d70947d49438a074f7c247c1 rdf:first sg:person.0637201450.26
116 rdf:rest N67668d5e3c8f45d8a9951a44a3e5bc04
117 N76293f90d2124dc491a3c26307de8c6e schema:name Springer Nature - SN SciGraph project
118 rdf:type schema:Organization
119 N7dd9df099f204517906cf58a7447de42 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Imaging, Three-Dimensional
121 rdf:type schema:DefinedTerm
122 N91d980d072da480b8e8808ffa1bde384 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Reproducibility of Results
124 rdf:type schema:DefinedTerm
125 N91e8fd4deccf4fa98d30677baa5e3ac0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Middle Aged
127 rdf:type schema:DefinedTerm
128 N9f4e0cd28d8d4997b1f15d47c9603642 schema:name pubmed_id
129 schema:value 24990346
130 rdf:type schema:PropertyValue
131 Na04b12baf263477db0c1a3e59f7376bc rdf:first sg:person.01251516646.45
132 rdf:rest N4e4b79c8e2da43fa848f574d38ad0a66
133 Na417516292714a6ba668d33971aca1de schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Sensitivity and Specificity
135 rdf:type schema:DefinedTerm
136 Nabc0925e773647c5a4aeea42af4bede4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Tomography, X-Ray Computed
138 rdf:type schema:DefinedTerm
139 Naeb14968ea5749d986e8aee1c195bbc2 schema:volumeNumber 27
140 rdf:type schema:PublicationVolume
141 Nc32666e0a81e42ddbbc55212ea2cc58f schema:name readcube_id
142 schema:value 891d20d466d75b22f047eb4184c5a5cac3b983145e688f0aab30ef51fd7921e0
143 rdf:type schema:PropertyValue
144 Nc7060ee2a453454892cad42f71bf4b80 rdf:first sg:person.011027760412.07
145 rdf:rest N6e640894d70947d49438a074f7c247c1
146 Nc863fa274a264200a4c1f7d78059d9dc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name Aged
148 rdf:type schema:DefinedTerm
149 Ncaf953e1e6a3499db7ab7b2e15ca70f0 schema:issueNumber 6
150 rdf:type schema:PublicationIssue
151 Ncde4acec65f7471db1ff5ae6ae2dba29 schema:name dimensions_id
152 schema:value pub.1028782764
153 rdf:type schema:PropertyValue
154 Nd03155d4a15f417fa8b87fc356443227 rdf:first sg:person.0671266353.78
155 rdf:rest N2027198e54f749288877e466c56f27dc
156 Nd0a450ceebb2450cb43cf96749ff1d96 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Algorithms
158 rdf:type schema:DefinedTerm
159 Nd9a3062df2ce4190af8b442726bbc841 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Female
161 rdf:type schema:DefinedTerm
162 Nddb398bf014a4ad09828c709493a9b46 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Image Processing, Computer-Assisted
164 rdf:type schema:DefinedTerm
165 Ndf85f8492c6245c286e649756be8dd03 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Male
167 rdf:type schema:DefinedTerm
168 Ne936c76a52d046cbae2f03fef744cbbb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Lung
170 rdf:type schema:DefinedTerm
171 Ne9af1e921e974e1fa0ac363fb15fabe8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Aged, 80 and over
173 rdf:type schema:DefinedTerm
174 Nef617cb357144e2f8a852961e4c008b8 schema:name Definiens AG, Bernhard-Wicki-Straße 5, 80636, Munchen, Germany
175 rdf:type schema:Organization
176 Nf853fb45c80842e0bc725d672bc030b1 rdf:first sg:person.01320333450.33
177 rdf:rest N186e9f6f04304846b0acb5f10379426d
178 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
179 schema:name Information and Computing Sciences
180 rdf:type schema:DefinedTerm
181 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
182 schema:name Artificial Intelligence and Image Processing
183 rdf:type schema:DefinedTerm
184 sg:grant.2438867 http://pending.schema.org/fundedItem sg:pub.10.1007/s10278-014-9716-x
185 rdf:type schema:MonetaryGrant
186 sg:grant.2689152 http://pending.schema.org/fundedItem sg:pub.10.1007/s10278-014-9716-x
187 rdf:type schema:MonetaryGrant
188 sg:journal.1100894 schema:issn 0897-1889
189 1618-727X
190 schema:name Journal of Digital Imaging
191 rdf:type schema:Periodical
192 sg:person.010462044607.31 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
193 schema:familyName Eschrich
194 schema:givenName Steven
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010462044607.31
196 rdf:type schema:Person
197 sg:person.011027760412.07 schema:affiliation https://www.grid.ac/institutes/grid.21729.3f
198 schema:familyName Schwartz
199 schema:givenName Lawrence H.
200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011027760412.07
201 rdf:type schema:Person
202 sg:person.01246004652.76 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
203 schema:familyName Balagurunathan
204 schema:givenName Yoganand
205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246004652.76
206 rdf:type schema:Person
207 sg:person.01251516646.45 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
208 schema:familyName Kim
209 schema:givenName Jongphil
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251516646.45
211 rdf:type schema:Person
212 sg:person.01251663701.28 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
213 schema:familyName Gatenby
214 schema:givenName Robert A.
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251663701.28
216 rdf:type schema:Person
217 sg:person.012516676542.25 schema:affiliation https://www.grid.ac/institutes/grid.170693.a
218 schema:familyName Hall
219 schema:givenName Lawrence O.
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012516676542.25
221 rdf:type schema:Person
222 sg:person.01267432547.98 schema:affiliation https://www.grid.ac/institutes/grid.21729.3f
223 schema:familyName Zhao
224 schema:givenName Binsheng
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01267432547.98
226 rdf:type schema:Person
227 sg:person.01300443404.06 schema:affiliation https://www.grid.ac/institutes/grid.411918.4
228 schema:familyName Wang
229 schema:givenName Hua
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01300443404.06
231 rdf:type schema:Person
232 sg:person.01320333450.33 schema:affiliation https://www.grid.ac/institutes/grid.170693.a
233 schema:familyName Goldgof
234 schema:givenName Dmitry B.
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320333450.33
236 rdf:type schema:Person
237 sg:person.01324730712.78 schema:affiliation https://www.grid.ac/institutes/grid.411918.4
238 schema:familyName Liu
239 schema:givenName Ying
240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324730712.78
241 rdf:type schema:Person
242 sg:person.01362605550.26 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
243 schema:familyName Gu
244 schema:givenName Yuhua
245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01362605550.26
246 rdf:type schema:Person
247 sg:person.014224135057.83 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
248 schema:familyName Gillies
249 schema:givenName Robert J.
250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014224135057.83
251 rdf:type schema:Person
252 sg:person.0637201450.26 schema:affiliation https://www.grid.ac/institutes/grid.170693.a
253 schema:familyName Basu
254 schema:givenName Satrajit
255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637201450.26
256 rdf:type schema:Person
257 sg:person.0671266353.78 schema:affiliation https://www.grid.ac/institutes/grid.468198.a
258 schema:familyName Kumar
259 schema:givenName Virendra
260 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0671266353.78
261 rdf:type schema:Person
262 sg:person.0754227410.45 schema:affiliation Nef617cb357144e2f8a852961e4c008b8
263 schema:familyName Korn
264 schema:givenName Rene
265 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0754227410.45
266 rdf:type schema:Person
267 sg:pub.10.1007/978-3-540-71331-9_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001716602
268 https://doi.org/10.1007/978-3-540-71331-9_15
269 rdf:type schema:CreativeWork
270 sg:pub.10.1007/s00330-011-2319-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010883759
271 https://doi.org/10.1007/s00330-011-2319-8
272 rdf:type schema:CreativeWork
273 sg:pub.10.1007/s10278-009-9185-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036249920
274 https://doi.org/10.1007/s10278-009-9185-9
275 rdf:type schema:CreativeWork
276 sg:pub.10.1038/nbt1306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048850628
277 https://doi.org/10.1038/nbt1306
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1016/0167-8655(94)90127-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052101887
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1016/j.acra.2008.10.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000654335
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1016/j.acra.2009.06.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003650188
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1016/j.artmed.2010.04.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038200455
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1016/j.lungcan.2010.03.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044013433
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1016/j.patcog.2012.10.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049617138
290 rdf:type schema:CreativeWork
291 https://doi.org/10.1093/bioinformatics/btm344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009424564
292 rdf:type schema:CreativeWork
293 https://doi.org/10.1093/jnci/djj403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007567909
294 rdf:type schema:CreativeWork
295 https://doi.org/10.1093/jnci/djq025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031376038
296 rdf:type schema:CreativeWork
297 https://doi.org/10.1097/00004728-200301000-00011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002181792
298 rdf:type schema:CreativeWork
299 https://doi.org/10.1102/1470-7330.2010.0021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045447398
300 rdf:type schema:CreativeWork
301 https://doi.org/10.1109/34.574797 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156543
302 rdf:type schema:CreativeWork
303 https://doi.org/10.1109/icsmc.2011.6083840 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094832561
304 rdf:type schema:CreativeWork
305 https://doi.org/10.1109/iembs.2005.1617159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077368930
306 rdf:type schema:CreativeWork
307 https://doi.org/10.1109/tbme.2008.919735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061527524
308 rdf:type schema:CreativeWork
309 https://doi.org/10.1111/1467-9868.00346 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018285816
310 rdf:type schema:CreativeWork
311 https://doi.org/10.1118/1.3140589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007720499
312 rdf:type schema:CreativeWork
313 https://doi.org/10.1148/radiol.2203001701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037493640
314 rdf:type schema:CreativeWork
315 https://doi.org/10.1148/radiol.2522081593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008413249
316 rdf:type schema:CreativeWork
317 https://doi.org/10.1148/rg.282075068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015440619
318 rdf:type schema:CreativeWork
319 https://doi.org/10.1155/2011/361589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005187391
320 rdf:type schema:CreativeWork
321 https://doi.org/10.1158/1078-0432.ccr-10-0125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032189617
322 rdf:type schema:CreativeWork
323 https://doi.org/10.1159/000068410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049121680
324 rdf:type schema:CreativeWork
325 https://doi.org/10.1164/rccm.200703-462pp schema:sameAs https://app.dimensions.ai/details/publication/pub.1023963564
326 rdf:type schema:CreativeWork
327 https://doi.org/10.2174/138620709789383196 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069174589
328 rdf:type schema:CreativeWork
329 https://doi.org/10.2307/2529310 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069974986
330 rdf:type schema:CreativeWork
331 https://doi.org/10.2307/2532031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977459
332 rdf:type schema:CreativeWork
333 https://doi.org/10.2307/2532051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977481
334 rdf:type schema:CreativeWork
335 https://www.grid.ac/institutes/grid.170693.a schema:alternateName University of South Florida
336 schema:name Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA
337 rdf:type schema:Organization
338 https://www.grid.ac/institutes/grid.21729.3f schema:alternateName Columbia University
339 schema:name Department of Radiology, Columbia University, New York, NY, USA
340 rdf:type schema:Organization
341 https://www.grid.ac/institutes/grid.411918.4 schema:alternateName Tianjin Medical University Cancer Institute and Hospital
342 schema:name Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
343 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
344 rdf:type schema:Organization
345 https://www.grid.ac/institutes/grid.468198.a schema:alternateName Moffitt Cancer Center
346 schema:name Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
347 Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
348 Experimental Imaging Program, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, SRB-2, 33612, Tampa, FL, USA
349 Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
350 rdf:type schema:Organization
 




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


...