Prognostic Value of Metabolic Tumor Volume on 11C-Methionine PET in Predicting Progression-Free Survival in High-Grade Glioma View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-08-29

AUTHORS

Min Young Yoo, Jin Chul Paeng, Gi Jeong Cheon, Dong Soo Lee, June-Key Chung, E. Edmund Kim, Keon Wook Kang

ABSTRACT

PurposeC-11 methionine (MET) PET is commonly used for diagnosing high-grade glioma (HGG). Recently, volumetric analysis has been widely applied to oncologic PET imaging. In this study, we investigated the prognostic value of metabolic tumor volume (MTV) on MET PET in HGG.MethodsA total of 30 patients with anaplastic astrocytoma (n = 12) and glioblastoma multiforme (n = 18) who underwent MET PET before treatment (surgery followed by chemo-radiotherapy) were retrospectively enrolled. Maximal tumor-to-normal brain ratio (TNRmax, maximum tumor activity divided by mean of normal tissue) and MTV (volume of tumor tissue that shows uptake >1.3-fold of mean uptake in normal tissue) were measured on MET PET. Adult patients were classified into two subgroups according to Radiation Therapy Oncology Group Recursive Partitioning Analysis (RTOG RPA) classification. Prognostic values of TNRmax, MTV and clinicopathologic factors were evaluated with regard to progression-free survival (PFS).ResultsMedian PFS of all patients was 7.9 months (range 1.0–53.8 months). In univariate analysis, MTV (cutoff 35 cm3) was a significant prognostic factor for PFS (P = 0.01), whereas TNRmax (cutoff 3.3) and RTOG RPA class were not (P = 0.80 and 0.61, respectively). Treatment of surgical resection exhibited a borderline significance (P = 0.06). In multivariate analysis, MTV was the only independent prognostic factor for PFS (P = 0.03).ConclusionMTV on MET PET is a significant and independent prognostic factor for PFS in HGG patients, whereas TNRmax is not. Thus, performing volumetric analysis of MET PET is recommended in HGG for better prognostication. More... »

PAGES

291-297

References to SciGraph publications

  • 2010-01-21. Correlation of l-methyl-11C-methionine (MET) uptake with l-type amino acid transporter 1 in human gliomas in JOURNAL OF NEURO-ONCOLOGY
  • 2014-09-06. Prognostic value of volumetric parameters of 18F-FDG PET in non-small-cell lung cancer: a meta-analysis in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 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
  • 2004-08-10. 11C-methionine PET as a prognostic marker in patients with glioma: comparison with 18F-FDG PET in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-07-04. [11C]-l-Methionine positron emission tomography in the management of children and young adults with brain tumors in JOURNAL OF NEURO-ONCOLOGY
  • 2015-03-11. Usefulness of MRI-assisted metabolic volumetric parameters provided by simultaneous 18F-fluorocholine PET/MRI for primary prostate cancer characterization in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2010-12-22. Radiotherapy and temozolomide for newly diagnosed glioblastoma and anaplastic astrocytoma: validation of Radiation Therapy Oncology Group-Recursive Partitioning Analysis in the IMRT and temozolomide era in JOURNAL OF NEURO-ONCOLOGY
  • 2009-08-07. Volumetry of [11C]-methionine PET uptake and MRI contrast enhancement in patients with recurrent glioblastoma multiforme in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2007-10-24. 11C-l-Methionine Positron Emission Tomography in the Clinical Management of Cerebral Gliomas in MOLECULAR IMAGING AND BIOLOGY
  • 2010-02-21. Prognostic significance of histological grading, p53 status, YKL-40 expression, and IDH1 mutations in pediatric high-grade gliomas in JOURNAL OF NEURO-ONCOLOGY
  • 2015-04-08. Prognostic value of volume-based measurements on 11C-methionine PET in glioma patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13139-015-0362-0

    DOI

    http://dx.doi.org/10.1007/s13139-015-0362-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1004683753

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26550048


    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/2008/11", 
            "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/2008/1103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Clinical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Oncology and Carcinogenesis", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yoo", 
            "givenName": "Min Young", 
            "id": "sg:person.01313717502.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313717502.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Paeng", 
            "givenName": "Jin Chul", 
            "id": "sg:person.01343511335.32", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01343511335.32"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
                "Cancer Research Institute, Seoul National University College of Medicine, Seoul, 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": "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, 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"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
                "Cancer Research Institute, Seoul National University College of Medicine, 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 Sciences, University of California, 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, Seoul National University, Seoul, Korea", 
                "Department of Radiological Sciences, University of California, Irvine, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kim", 
            "givenName": "E. Edmund", 
            "id": "sg:person.0656745142.66", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656745142.66"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea", 
                "Cancer Research Institute, Seoul National University College of Medicine, 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"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s11060-010-0117-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015792605", 
              "https://doi.org/10.1007/s11060-010-0117-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11307-007-0115-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028556870", 
              "https://doi.org/10.1007/s11307-007-0115-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-004-1598-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033967191", 
              "https://doi.org/10.1007/s00259-004-1598-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3046-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003573749", 
              "https://doi.org/10.1007/s00259-015-3046-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1219-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000082347", 
              "https://doi.org/10.1007/s00259-009-1219-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-014-2903-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007455399", 
              "https://doi.org/10.1007/s00259-014-2903-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3026-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042483529", 
              "https://doi.org/10.1007/s00259-015-3026-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11060-010-0499-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000761937", 
              "https://doi.org/10.1007/s11060-010-0499-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11060-010-0129-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025034182", 
              "https://doi.org/10.1007/s11060-010-0129-5"
            ], 
            "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/s11060-009-9953-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043008218", 
              "https://doi.org/10.1007/s11060-009-9953-x"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-08-29", 
        "datePublishedReg": "2015-08-29", 
        "description": "PurposeC-11 methionine (MET) PET is commonly used for diagnosing high-grade glioma (HGG). Recently, volumetric analysis has been widely applied to oncologic PET imaging. In this study, we investigated the prognostic value of metabolic tumor volume (MTV) on MET PET in HGG.MethodsA total of 30 patients with anaplastic astrocytoma (n\u2009=\u200912) and glioblastoma multiforme (n\u2009=\u200918) who underwent MET PET before treatment (surgery followed by chemo-radiotherapy) were retrospectively enrolled. Maximal tumor-to-normal brain ratio (TNRmax, maximum tumor activity divided by mean of normal tissue) and MTV (volume of tumor tissue that shows uptake\u2009>1.3-fold of mean uptake in normal tissue) were measured on MET PET. Adult patients were classified into two subgroups according to Radiation Therapy Oncology Group Recursive Partitioning Analysis (RTOG RPA) classification. Prognostic values of TNRmax, MTV and clinicopathologic factors were evaluated with regard to progression-free survival (PFS).ResultsMedian PFS of all patients was 7.9\u00a0months (range 1.0\u201353.8\u00a0months). In univariate analysis, MTV (cutoff 35\u00a0cm3) was a significant prognostic factor for PFS (P\u2009=\u20090.01), whereas TNRmax (cutoff 3.3) and RTOG RPA class were not (P\u2009=\u20090.80 and 0.61, respectively). Treatment of surgical resection exhibited a borderline significance (P\u2009=\u20090.06). In multivariate analysis, MTV was the only independent prognostic factor for PFS (P\u2009=\u20090.03).ConclusionMTV on MET PET is a significant and independent prognostic factor for PFS in HGG patients, whereas TNRmax is not. Thus, performing volumetric analysis of MET PET is recommended in HGG for better prognostication.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s13139-015-0362-0", 
        "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": "49"
          }
        ], 
        "keywords": [
          "progression-free survival", 
          "metabolic tumor volume", 
          "high-grade gliomas", 
          "independent prognostic factor", 
          "MET-PET", 
          "prognostic factors", 
          "prognostic value", 
          "tumor volume", 
          "recursive partitioning analysis (RPA) classification", 
          "ResultsMedian progression-free survival", 
          "Predicting Progression-Free Survival", 
          "only independent prognostic factor", 
          "RTOG-RPA class", 
          "significant prognostic factors", 
          "normal brain ratio", 
          "volumetric analysis", 
          "RPA class", 
          "surgical resection", 
          "adult patients", 
          "clinicopathologic factors", 
          "brain ratio", 
          "methionine PET", 
          "borderline significance", 
          "better prognostication", 
          "HGG patients", 
          "univariate analysis", 
          "anaplastic astrocytoma", 
          "maximal tumor", 
          "glioblastoma multiforme", 
          "oncologic PET imaging", 
          "patients", 
          "multivariate analysis", 
          "PET imaging", 
          "gliomas", 
          "survival", 
          "treatment", 
          "PET", 
          "TNRmax", 
          "ConclusionMTV", 
          "resection", 
          "factors", 
          "astrocytomas", 
          "tumors", 
          "multiforme", 
          "prognostication", 
          "analysis classification", 
          "months", 
          "subgroups", 
          "total", 
          "imaging", 
          "volume", 
          "analysis", 
          "significance", 
          "study", 
          "regard", 
          "values", 
          "classification", 
          "ratio", 
          "class"
        ], 
        "name": "Prognostic Value of Metabolic Tumor Volume on 11C-Methionine PET in Predicting Progression-Free Survival in High-Grade Glioma", 
        "pagination": "291-297", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1004683753"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s13139-015-0362-0"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "26550048"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s13139-015-0362-0", 
          "https://app.dimensions.ai/details/publication/pub.1004683753"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-11-24T20:59", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_651.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s13139-015-0362-0"
      }
    ]
     

    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/s13139-015-0362-0'

    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/s13139-015-0362-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13139-015-0362-0'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13139-015-0362-0'


     

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

    220 TRIPLES      21 PREDICATES      96 URIs      76 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s13139-015-0362-0 schema:about anzsrc-for:11
    2 anzsrc-for:1103
    3 anzsrc-for:1112
    4 schema:author N99310aca15f549c4a253741e3a6fadb1
    5 schema:citation sg:pub.10.1007/s00259-004-1598-6
    6 sg:pub.10.1007/s00259-009-1219-5
    7 sg:pub.10.1007/s00259-014-2903-7
    8 sg:pub.10.1007/s00259-015-3026-5
    9 sg:pub.10.1007/s00259-015-3046-1
    10 sg:pub.10.1007/s11060-009-9953-x
    11 sg:pub.10.1007/s11060-010-0117-9
    12 sg:pub.10.1007/s11060-010-0129-5
    13 sg:pub.10.1007/s11060-010-0499-8
    14 sg:pub.10.1007/s11307-007-0115-2
    15 sg:pub.10.1007/s13139-012-0181-5
    16 schema:datePublished 2015-08-29
    17 schema:datePublishedReg 2015-08-29
    18 schema:description PurposeC-11 methionine (MET) PET is commonly used for diagnosing high-grade glioma (HGG). Recently, volumetric analysis has been widely applied to oncologic PET imaging. In this study, we investigated the prognostic value of metabolic tumor volume (MTV) on MET PET in HGG.MethodsA total of 30 patients with anaplastic astrocytoma (n = 12) and glioblastoma multiforme (n = 18) who underwent MET PET before treatment (surgery followed by chemo-radiotherapy) were retrospectively enrolled. Maximal tumor-to-normal brain ratio (TNRmax, maximum tumor activity divided by mean of normal tissue) and MTV (volume of tumor tissue that shows uptake >1.3-fold of mean uptake in normal tissue) were measured on MET PET. Adult patients were classified into two subgroups according to Radiation Therapy Oncology Group Recursive Partitioning Analysis (RTOG RPA) classification. Prognostic values of TNRmax, MTV and clinicopathologic factors were evaluated with regard to progression-free survival (PFS).ResultsMedian PFS of all patients was 7.9 months (range 1.0–53.8 months). In univariate analysis, MTV (cutoff 35 cm3) was a significant prognostic factor for PFS (P = 0.01), whereas TNRmax (cutoff 3.3) and RTOG RPA class were not (P = 0.80 and 0.61, respectively). Treatment of surgical resection exhibited a borderline significance (P = 0.06). In multivariate analysis, MTV was the only independent prognostic factor for PFS (P = 0.03).ConclusionMTV on MET PET is a significant and independent prognostic factor for PFS in HGG patients, whereas TNRmax is not. Thus, performing volumetric analysis of MET PET is recommended in HGG for better prognostication.
    19 schema:genre article
    20 schema:isAccessibleForFree true
    21 schema:isPartOf N32081c516796472a90bb4f797177a347
    22 N49c2ab1a8d904c45b9463e972c11f84a
    23 sg:journal.1016285
    24 schema:keywords ConclusionMTV
    25 HGG patients
    26 MET-PET
    27 PET
    28 PET imaging
    29 Predicting Progression-Free Survival
    30 RPA class
    31 RTOG-RPA class
    32 ResultsMedian progression-free survival
    33 TNRmax
    34 adult patients
    35 analysis
    36 analysis classification
    37 anaplastic astrocytoma
    38 astrocytomas
    39 better prognostication
    40 borderline significance
    41 brain ratio
    42 class
    43 classification
    44 clinicopathologic factors
    45 factors
    46 glioblastoma multiforme
    47 gliomas
    48 high-grade gliomas
    49 imaging
    50 independent prognostic factor
    51 maximal tumor
    52 metabolic tumor volume
    53 methionine PET
    54 months
    55 multiforme
    56 multivariate analysis
    57 normal brain ratio
    58 oncologic PET imaging
    59 only independent prognostic factor
    60 patients
    61 prognostic factors
    62 prognostic value
    63 prognostication
    64 progression-free survival
    65 ratio
    66 recursive partitioning analysis (RPA) classification
    67 regard
    68 resection
    69 significance
    70 significant prognostic factors
    71 study
    72 subgroups
    73 surgical resection
    74 survival
    75 total
    76 treatment
    77 tumor volume
    78 tumors
    79 univariate analysis
    80 values
    81 volume
    82 volumetric analysis
    83 schema:name Prognostic Value of Metabolic Tumor Volume on 11C-Methionine PET in Predicting Progression-Free Survival in High-Grade Glioma
    84 schema:pagination 291-297
    85 schema:productId N00539dc205e345e6a70643e1c1646bad
    86 N7ff181f128484150ac55451d38c721d2
    87 N9f1517e2606d4099a5935846ca91548b
    88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004683753
    89 https://doi.org/10.1007/s13139-015-0362-0
    90 schema:sdDatePublished 2022-11-24T20:59
    91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    92 schema:sdPublisher Nb8dbf1f88a1b4c6297bcd9acab7ad300
    93 schema:url https://doi.org/10.1007/s13139-015-0362-0
    94 sgo:license sg:explorer/license/
    95 sgo:sdDataset articles
    96 rdf:type schema:ScholarlyArticle
    97 N00539dc205e345e6a70643e1c1646bad schema:name pubmed_id
    98 schema:value 26550048
    99 rdf:type schema:PropertyValue
    100 N2c76366ae6df40469355e12ded1491c0 rdf:first sg:person.01333472502.31
    101 rdf:rest Na8ebd7e3bef3485da879e5e4125e9057
    102 N32081c516796472a90bb4f797177a347 schema:volumeNumber 49
    103 rdf:type schema:PublicationVolume
    104 N3aefa42197ac468d9a94df51ed508366 rdf:first sg:person.0656745142.66
    105 rdf:rest N7e3fb5fbb7db44338f157dd51f893656
    106 N49c2ab1a8d904c45b9463e972c11f84a schema:issueNumber 4
    107 rdf:type schema:PublicationIssue
    108 N7e3fb5fbb7db44338f157dd51f893656 rdf:first sg:person.0761746266.86
    109 rdf:rest rdf:nil
    110 N7ff181f128484150ac55451d38c721d2 schema:name doi
    111 schema:value 10.1007/s13139-015-0362-0
    112 rdf:type schema:PropertyValue
    113 N99310aca15f549c4a253741e3a6fadb1 rdf:first sg:person.01313717502.49
    114 rdf:rest Nbc7c9d705fdd403c97ce0f814c8112b0
    115 N9f1517e2606d4099a5935846ca91548b schema:name dimensions_id
    116 schema:value pub.1004683753
    117 rdf:type schema:PropertyValue
    118 Na8ebd7e3bef3485da879e5e4125e9057 rdf:first sg:person.015617314175.88
    119 rdf:rest Nba80209494564e2ea1721908f0b6ba06
    120 Nb8dbf1f88a1b4c6297bcd9acab7ad300 schema:name Springer Nature - SN SciGraph project
    121 rdf:type schema:Organization
    122 Nba80209494564e2ea1721908f0b6ba06 rdf:first sg:person.0751347234.39
    123 rdf:rest N3aefa42197ac468d9a94df51ed508366
    124 Nbc7c9d705fdd403c97ce0f814c8112b0 rdf:first sg:person.01343511335.32
    125 rdf:rest N2c76366ae6df40469355e12ded1491c0
    126 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    127 schema:name Medical and Health Sciences
    128 rdf:type schema:DefinedTerm
    129 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    130 schema:name Clinical Sciences
    131 rdf:type schema:DefinedTerm
    132 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    133 schema:name Oncology and Carcinogenesis
    134 rdf:type schema:DefinedTerm
    135 sg:journal.1016285 schema:issn 1869-3474
    136 1869-3482
    137 schema:name Nuclear Medicine and Molecular Imaging
    138 schema:publisher Springer Nature
    139 rdf:type schema:Periodical
    140 sg:person.01313717502.49 schema:affiliation grid-institutes:grid.412484.f
    141 schema:familyName Yoo
    142 schema:givenName Min Young
    143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313717502.49
    144 rdf:type schema:Person
    145 sg:person.01333472502.31 schema:affiliation grid-institutes:grid.31501.36
    146 schema:familyName Cheon
    147 schema:givenName Gi Jeong
    148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01333472502.31
    149 rdf:type schema:Person
    150 sg:person.01343511335.32 schema:affiliation grid-institutes:grid.412484.f
    151 schema:familyName Paeng
    152 schema:givenName Jin Chul
    153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01343511335.32
    154 rdf:type schema:Person
    155 sg:person.015617314175.88 schema:affiliation grid-institutes:grid.31501.36
    156 schema:familyName Lee
    157 schema:givenName Dong Soo
    158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88
    159 rdf:type schema:Person
    160 sg:person.0656745142.66 schema:affiliation grid-institutes:grid.266093.8
    161 schema:familyName Kim
    162 schema:givenName E. Edmund
    163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656745142.66
    164 rdf:type schema:Person
    165 sg:person.0751347234.39 schema:affiliation grid-institutes:grid.31501.36
    166 schema:familyName Chung
    167 schema:givenName June-Key
    168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751347234.39
    169 rdf:type schema:Person
    170 sg:person.0761746266.86 schema:affiliation grid-institutes:grid.31501.36
    171 schema:familyName Kang
    172 schema:givenName Keon Wook
    173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761746266.86
    174 rdf:type schema:Person
    175 sg:pub.10.1007/s00259-004-1598-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033967191
    176 https://doi.org/10.1007/s00259-004-1598-6
    177 rdf:type schema:CreativeWork
    178 sg:pub.10.1007/s00259-009-1219-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000082347
    179 https://doi.org/10.1007/s00259-009-1219-5
    180 rdf:type schema:CreativeWork
    181 sg:pub.10.1007/s00259-014-2903-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007455399
    182 https://doi.org/10.1007/s00259-014-2903-7
    183 rdf:type schema:CreativeWork
    184 sg:pub.10.1007/s00259-015-3026-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042483529
    185 https://doi.org/10.1007/s00259-015-3026-5
    186 rdf:type schema:CreativeWork
    187 sg:pub.10.1007/s00259-015-3046-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003573749
    188 https://doi.org/10.1007/s00259-015-3046-1
    189 rdf:type schema:CreativeWork
    190 sg:pub.10.1007/s11060-009-9953-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1043008218
    191 https://doi.org/10.1007/s11060-009-9953-x
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1007/s11060-010-0117-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015792605
    194 https://doi.org/10.1007/s11060-010-0117-9
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1007/s11060-010-0129-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025034182
    197 https://doi.org/10.1007/s11060-010-0129-5
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1007/s11060-010-0499-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000761937
    200 https://doi.org/10.1007/s11060-010-0499-8
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1007/s11307-007-0115-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028556870
    203 https://doi.org/10.1007/s11307-007-0115-2
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1007/s13139-012-0181-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052133048
    206 https://doi.org/10.1007/s13139-012-0181-5
    207 rdf:type schema:CreativeWork
    208 grid-institutes:grid.266093.8 schema:alternateName Department of Radiological Sciences, University of California, Irvine, CA, USA
    209 schema:name Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
    210 Department of Radiological Sciences, University of California, Irvine, CA, USA
    211 rdf:type schema:Organization
    212 grid-institutes:grid.31501.36 schema:alternateName Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
    213 Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
    214 schema:name Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
    215 Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
    216 Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea
    217 rdf:type schema:Organization
    218 grid-institutes:grid.412484.f schema:alternateName Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea
    219 schema:name Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, 110-744, Seoul, Jongno-gu,, Korea
    220 rdf:type schema:Organization
     




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


    ...