Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-06-11

AUTHORS

Seunggyun Ha, Hongyoon Choi, Gi Jeong Cheon, Keon Wook Kang, June-Key Chung, Euishin Edmund Kim, Dong Soo Lee

ABSTRACT

PurposeTexture analysis on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC).MethodsDiagnostic 18F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogram-based algorithms, absolute gradient-based algorithms, run-length matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery.ResultsFifteen texture features had significant different values between ADC and SqCC. LDA with 24 automate-selected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUVmax and texture parameters (|r| ≤ 0.62).ConclusionEach subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker. More... »

PAGES

278-286

References to SciGraph publications

  • 2012-11-17. Usefulness of Combined Metabolic–Volumetric Indices of 18F-FDG PET/CT for the Early Prediction of Neoadjuvant Chemotherapy Outcomes in Breast Cancer in NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-08-13. The promise and limits of PET texture analysis in ANNALS OF NUCLEAR MEDICINE
  • 2012-10-13. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-03-02. Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-08-21. Heterogeneity Analysis of 18F-FDG Uptake in Differentiating Between Metastatic and Inflammatory Lymph Nodes in Adenocarcinoma of the Lung: Comparison with Other Parameters and its Application in a Clinical Setting in NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2014-01-22. Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology in NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2012-10-24. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? in INSIGHTS INTO IMAGING
  • 2013-01-17. Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography in JOURNAL OF DIGITAL IMAGING
  • 2013-07-24. Total lesion glycolysis by 18F-FDG PET/CT is a reliable predictor of prognosis in soft-tissue sarcoma in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13139-014-0283-3

    DOI

    http://dx.doi.org/10.1007/s13139-014-0283-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1038440461

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26396632


    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/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing 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"
          }, 
          {
            "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/2008/1103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Clinical Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ha", 
            "givenName": "Seunggyun", 
            "id": "sg:person.01107464404.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107464404.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Choi", 
            "givenName": "Hongyoon", 
            "id": "sg:person.0631257534.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, 110-744, Seoul, Jongno-Gu, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Cancer Research Institute, Seoul National University, Seoul, Korea", 
                "Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, 110-744, Seoul, Jongno-Gu, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cheon", 
            "givenName": "Gi Jeong", 
            "id": "sg:person.01333472502.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333472502.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Research Institute, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Cancer Research Institute, Seoul National University, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kang", 
            "givenName": "Keon Wook", 
            "id": "sg:person.0761746266.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761746266.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Research Institute, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Cancer Research Institute, Seoul National University, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chung", 
            "givenName": "June-Key", 
            "id": "sg:person.0751347234.39", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751347234.39"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiological Science, University of California at Irvine, Irvine, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.266093.8", 
              "name": [
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea", 
                "Department of Radiological Science, University of California at Irvine, Irvine, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kim", 
            "givenName": "Euishin Edmund", 
            "id": "sg:person.0656745142.66", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656745142.66"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Dong Soo", 
            "id": "sg:person.015617314175.88", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s13244-012-0196-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036528660", 
              "https://doi.org/10.1007/s13244-012-0196-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12149-013-0759-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038385863", 
              "https://doi.org/10.1007/s12149-013-0759-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-011-1755-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051740136", 
              "https://doi.org/10.1007/s00259-011-1755-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10278-012-9547-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041174072", 
              "https://doi.org/10.1007/s10278-012-9547-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2511-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039775122", 
              "https://doi.org/10.1007/s00259-013-2511-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13139-013-0260-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046932155", 
              "https://doi.org/10.1007/s13139-013-0260-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13139-013-0216-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037562362", 
              "https://doi.org/10.1007/s13139-013-0216-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13139-012-0181-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052133048", 
              "https://doi.org/10.1007/s13139-012-0181-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-012-2247-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051316850", 
              "https://doi.org/10.1007/s00259-012-2247-0"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014-06-11", 
        "datePublishedReg": "2014-06-11", 
        "description": "PurposeTexture analysis on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC).MethodsDiagnostic 18F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogram-based algorithms, absolute gradient-based algorithms, run-length matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery.ResultsFifteen texture features had significant different values between ADC and SqCC. LDA with 24 automate-selected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUVmax and texture parameters (|r|\u2009\u2264\u20090.62).ConclusionEach subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s13139-014-0283-3", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1016285", 
            "issn": [
              "1869-3474", 
              "1869-3482"
            ], 
            "name": "Nuclear Medicine and Molecular Imaging", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "48"
          }
        ], 
        "keywords": [
          "matrix-based algorithm", 
          "texture features", 
          "non-small cell lung carcinoma (NSCLC) subtypes", 
          "linear discriminant analysis", 
          "lung carcinoma subtypes", 
          "model-based algorithm", 
          "classification error probability", 
          "tomography scan", 
          "gradient-based algorithm", 
          "carcinoma subtypes", 
          "positron emission tomography scan", 
          "Fisher coefficient", 
          "mutual information", 
          "emission tomography scan", 
          "discriminating features", 
          "squamous cell carcinoma", 
          "region of interest", 
          "algorithm", 
          "analysis tools", 
          "high SUV values", 
          "linear separability", 
          "clustering of tumors", 
          "texture analysis", 
          "metabolic heterogeneity", 
          "FDG-PET", 
          "cell carcinoma", 
          "histopathologic examination", 
          "NSCLC tumors", 
          "tumor subtypes", 
          "adenocarcinoma", 
          "PET/", 
          "SUV values", 
          "error probability", 
          "texture parameters", 
          "subtypes", 
          "tumors", 
          "SqCC", 
          "transverse images", 
          "features", 
          "discriminant analysis", 
          "scans", 
          "clustering", 
          "histogram", 
          "preliminary results", 
          "images", 
          "NSCLC", 
          "patients", 
          "biopsy", 
          "surgery", 
          "SUVmax", 
          "carcinoma", 
          "PET", 
          "information", 
          "separability", 
          "significant different values", 
          "biomarkers", 
          "tool", 
          "minimization", 
          "examination", 
          "high correlation", 
          "average correlation", 
          "correlation", 
          "heterogeneity", 
          "hundreds", 
          "differences", 
          "results", 
          "parameters", 
          "analysis", 
          "study", 
          "probability", 
          "interest", 
          "values", 
          "potential", 
          "different values", 
          "characteristics", 
          "textural parameters", 
          "region", 
          "textural characteristics", 
          "coefficient"
        ], 
        "name": "Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result", 
        "pagination": "278-286", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1038440461"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s13139-014-0283-3"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "26396632"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s13139-014-0283-3", 
          "https://app.dimensions.ai/details/publication/pub.1038440461"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:40", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_630.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s13139-014-0283-3"
      }
    ]
     

    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.10.1007/s13139-014-0283-3'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13139-014-0283-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13139-014-0283-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13139-014-0283-3'


     

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

    235 TRIPLES      21 PREDICATES      115 URIs      96 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s13139-014-0283-3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 anzsrc-for:11
    4 anzsrc-for:1103
    5 schema:author Ne32f04adc8934f6ab62d6e5343b55d9a
    6 schema:citation sg:pub.10.1007/s00259-011-1755-7
    7 sg:pub.10.1007/s00259-012-2247-0
    8 sg:pub.10.1007/s00259-013-2511-y
    9 sg:pub.10.1007/s10278-012-9547-6
    10 sg:pub.10.1007/s12149-013-0759-8
    11 sg:pub.10.1007/s13139-012-0181-5
    12 sg:pub.10.1007/s13139-013-0216-6
    13 sg:pub.10.1007/s13139-013-0260-2
    14 sg:pub.10.1007/s13244-012-0196-6
    15 schema:datePublished 2014-06-11
    16 schema:datePublishedReg 2014-06-11
    17 schema:description PurposeTexture analysis on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC).MethodsDiagnostic 18F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogram-based algorithms, absolute gradient-based algorithms, run-length matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery.ResultsFifteen texture features had significant different values between ADC and SqCC. LDA with 24 automate-selected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUVmax and texture parameters (|r| ≤ 0.62).ConclusionEach subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker.
    18 schema:genre article
    19 schema:isAccessibleForFree true
    20 schema:isPartOf N25f340a725554ef88fc86e0ee582f21c
    21 N3f522f70662c4ae79a6311d183a573cb
    22 sg:journal.1016285
    23 schema:keywords FDG-PET
    24 Fisher coefficient
    25 NSCLC
    26 NSCLC tumors
    27 PET
    28 PET/
    29 SUV values
    30 SUVmax
    31 SqCC
    32 adenocarcinoma
    33 algorithm
    34 analysis
    35 analysis tools
    36 average correlation
    37 biomarkers
    38 biopsy
    39 carcinoma
    40 carcinoma subtypes
    41 cell carcinoma
    42 characteristics
    43 classification error probability
    44 clustering
    45 clustering of tumors
    46 coefficient
    47 correlation
    48 differences
    49 different values
    50 discriminant analysis
    51 discriminating features
    52 emission tomography scan
    53 error probability
    54 examination
    55 features
    56 gradient-based algorithm
    57 heterogeneity
    58 high SUV values
    59 high correlation
    60 histogram
    61 histopathologic examination
    62 hundreds
    63 images
    64 information
    65 interest
    66 linear discriminant analysis
    67 linear separability
    68 lung carcinoma subtypes
    69 matrix-based algorithm
    70 metabolic heterogeneity
    71 minimization
    72 model-based algorithm
    73 mutual information
    74 non-small cell lung carcinoma (NSCLC) subtypes
    75 parameters
    76 patients
    77 positron emission tomography scan
    78 potential
    79 preliminary results
    80 probability
    81 region
    82 region of interest
    83 results
    84 scans
    85 separability
    86 significant different values
    87 squamous cell carcinoma
    88 study
    89 subtypes
    90 surgery
    91 textural characteristics
    92 textural parameters
    93 texture analysis
    94 texture features
    95 texture parameters
    96 tomography scan
    97 tool
    98 transverse images
    99 tumor subtypes
    100 tumors
    101 values
    102 schema:name Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result
    103 schema:pagination 278-286
    104 schema:productId Nb487d8ce87cd4cb7b3680c735a7c7c1c
    105 Nc78e117cdf2f413eb7476455e4920205
    106 Ndfc1b9f9926d47b7a67e7644f555b61b
    107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038440461
    108 https://doi.org/10.1007/s13139-014-0283-3
    109 schema:sdDatePublished 2022-10-01T06:40
    110 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    111 schema:sdPublisher Nb820b4d77a854a069b0dc02a32f6acde
    112 schema:url https://doi.org/10.1007/s13139-014-0283-3
    113 sgo:license sg:explorer/license/
    114 sgo:sdDataset articles
    115 rdf:type schema:ScholarlyArticle
    116 N25f340a725554ef88fc86e0ee582f21c schema:volumeNumber 48
    117 rdf:type schema:PublicationVolume
    118 N2ad237a1b30b4dd78e876ae834628bc8 rdf:first sg:person.0751347234.39
    119 rdf:rest N3aefd3ff4d8844e1bebbde9b775230d6
    120 N2ccebb52d97f497bb896259d9cdfa80a rdf:first sg:person.0631257534.28
    121 rdf:rest N6beee19031734c13b37f033410d9f4a2
    122 N3aefd3ff4d8844e1bebbde9b775230d6 rdf:first sg:person.0656745142.66
    123 rdf:rest Ndb89e55e8ff440bb824bc31b4a759f2f
    124 N3f522f70662c4ae79a6311d183a573cb schema:issueNumber 4
    125 rdf:type schema:PublicationIssue
    126 N6beee19031734c13b37f033410d9f4a2 rdf:first sg:person.01333472502.31
    127 rdf:rest Nbac79acfd4b74612953abefe461052af
    128 Nb487d8ce87cd4cb7b3680c735a7c7c1c schema:name dimensions_id
    129 schema:value pub.1038440461
    130 rdf:type schema:PropertyValue
    131 Nb820b4d77a854a069b0dc02a32f6acde schema:name Springer Nature - SN SciGraph project
    132 rdf:type schema:Organization
    133 Nbac79acfd4b74612953abefe461052af rdf:first sg:person.0761746266.86
    134 rdf:rest N2ad237a1b30b4dd78e876ae834628bc8
    135 Nc78e117cdf2f413eb7476455e4920205 schema:name pubmed_id
    136 schema:value 26396632
    137 rdf:type schema:PropertyValue
    138 Ndb89e55e8ff440bb824bc31b4a759f2f rdf:first sg:person.015617314175.88
    139 rdf:rest rdf:nil
    140 Ndfc1b9f9926d47b7a67e7644f555b61b schema:name doi
    141 schema:value 10.1007/s13139-014-0283-3
    142 rdf:type schema:PropertyValue
    143 Ne32f04adc8934f6ab62d6e5343b55d9a rdf:first sg:person.01107464404.68
    144 rdf:rest N2ccebb52d97f497bb896259d9cdfa80a
    145 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    146 schema:name Information and Computing Sciences
    147 rdf:type schema:DefinedTerm
    148 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    149 schema:name Artificial Intelligence and Image Processing
    150 rdf:type schema:DefinedTerm
    151 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    152 schema:name Medical and Health Sciences
    153 rdf:type schema:DefinedTerm
    154 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    155 schema:name Clinical Sciences
    156 rdf:type schema:DefinedTerm
    157 sg:journal.1016285 schema:issn 1869-3474
    158 1869-3482
    159 schema:name Nuclear Medicine and Molecular Imaging
    160 schema:publisher Springer Nature
    161 rdf:type schema:Periodical
    162 sg:person.01107464404.68 schema:affiliation grid-institutes:grid.31501.36
    163 schema:familyName Ha
    164 schema:givenName Seunggyun
    165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107464404.68
    166 rdf:type schema:Person
    167 sg:person.01333472502.31 schema:affiliation grid-institutes:grid.31501.36
    168 schema:familyName Cheon
    169 schema:givenName Gi Jeong
    170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333472502.31
    171 rdf:type schema:Person
    172 sg:person.015617314175.88 schema:affiliation grid-institutes:grid.31501.36
    173 schema:familyName Lee
    174 schema:givenName Dong Soo
    175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88
    176 rdf:type schema:Person
    177 sg:person.0631257534.28 schema:affiliation grid-institutes:grid.31501.36
    178 schema:familyName Choi
    179 schema:givenName Hongyoon
    180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28
    181 rdf:type schema:Person
    182 sg:person.0656745142.66 schema:affiliation grid-institutes:grid.266093.8
    183 schema:familyName Kim
    184 schema:givenName Euishin Edmund
    185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656745142.66
    186 rdf:type schema:Person
    187 sg:person.0751347234.39 schema:affiliation grid-institutes:grid.31501.36
    188 schema:familyName Chung
    189 schema:givenName June-Key
    190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751347234.39
    191 rdf:type schema:Person
    192 sg:person.0761746266.86 schema:affiliation grid-institutes:grid.31501.36
    193 schema:familyName Kang
    194 schema:givenName Keon Wook
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761746266.86
    196 rdf:type schema:Person
    197 sg:pub.10.1007/s00259-011-1755-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051740136
    198 https://doi.org/10.1007/s00259-011-1755-7
    199 rdf:type schema:CreativeWork
    200 sg:pub.10.1007/s00259-012-2247-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051316850
    201 https://doi.org/10.1007/s00259-012-2247-0
    202 rdf:type schema:CreativeWork
    203 sg:pub.10.1007/s00259-013-2511-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1039775122
    204 https://doi.org/10.1007/s00259-013-2511-y
    205 rdf:type schema:CreativeWork
    206 sg:pub.10.1007/s10278-012-9547-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041174072
    207 https://doi.org/10.1007/s10278-012-9547-6
    208 rdf:type schema:CreativeWork
    209 sg:pub.10.1007/s12149-013-0759-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038385863
    210 https://doi.org/10.1007/s12149-013-0759-8
    211 rdf:type schema:CreativeWork
    212 sg:pub.10.1007/s13139-012-0181-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052133048
    213 https://doi.org/10.1007/s13139-012-0181-5
    214 rdf:type schema:CreativeWork
    215 sg:pub.10.1007/s13139-013-0216-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037562362
    216 https://doi.org/10.1007/s13139-013-0216-6
    217 rdf:type schema:CreativeWork
    218 sg:pub.10.1007/s13139-013-0260-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046932155
    219 https://doi.org/10.1007/s13139-013-0260-2
    220 rdf:type schema:CreativeWork
    221 sg:pub.10.1007/s13244-012-0196-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036528660
    222 https://doi.org/10.1007/s13244-012-0196-6
    223 rdf:type schema:CreativeWork
    224 grid-institutes:grid.266093.8 schema:alternateName Department of Radiological Science, University of California at Irvine, Irvine, CA, USA
    225 schema:name Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
    226 Department of Radiological Science, University of California at Irvine, Irvine, CA, USA
    227 rdf:type schema:Organization
    228 grid-institutes:grid.31501.36 schema:alternateName Cancer Research Institute, Seoul National University, Seoul, Korea
    229 Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
    230 Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, 110-744, Seoul, Jongno-Gu, Korea
    231 schema:name Cancer Research Institute, Seoul National University, Seoul, Korea
    232 Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
    233 Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, 110-744, Seoul, Jongno-Gu, Korea
    234 Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea
    235 rdf:type schema:Organization
     




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


    ...