Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-27

AUTHORS

Jinyeong Choi, Azmal Sarker, Hongyoon Choi, Dong Soo Lee, Hyung-Jun Im

ABSTRACT

BackgroundHigh levels of 18F-fluorodeoxyglucose (18F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of 18F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.ResultsUsing an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In 18F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower 18F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of 18F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).ConclusionsA high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma. More... »

PAGES

38

References to SciGraph publications

  • 2011-12-21. Cancer immunotherapy comes of age in NATURE
  • 2019-09-16. 18F-FDG uptake in PET/CT is a potential predictive biomarker of response to anti-PD-1 antibody therapy in non-small cell lung cancer in SCIENTIFIC REPORTS
  • 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
  • 2020-01-31. Simplified molecular classification of lung adenocarcinomas based on EGFR, KRAS, and TP53 mutations in BMC CANCER
  • 2018-10-11. A molecular and staging model predicts survival in patients with resected non-small cell lung cancer in BMC CANCER
  • 2014-07-09. Comprehensive molecular profiling of lung adenocarcinoma in NATURE
  • 2016-06-01. Correlation of metabolic information on FDG-PET with tissue expression of immune markers in patients with non-small cell lung cancer (NSCLC) who are candidates for upfront surgery in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2018-05-16. Cytolytic Activity Score to Assess Anticancer Immunity in Colorectal Cancer in ANNALS OF SURGICAL ONCOLOGY
  • 2019-11-21. Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis in EJNMMI RESEARCH
  • 2007-11-18. Adaptive immunity maintains occult cancer in an equilibrium state in NATURE
  • 2019-09-09. New insight on the correlation of metabolic status on 18F-FDG PET/CT with immune marker expression in patients with non-small cell lung cancer in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2018-01-08. Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck in SCIENTIFIC REPORTS
  • 2019-01-09. Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles in SCIENTIFIC REPORTS
  • 2018-06-25. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing in NATURE MEDICINE
  • 2018-10-16. A radiogenomic dataset of non-small cell lung cancer in SCIENTIFIC DATA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13550-022-00908-9

    DOI

    http://dx.doi.org/10.1186/s13550-022-00908-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149003249

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35759068


    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/2008/1107", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Immunology", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Choi", 
            "givenName": "Jinyeong", 
            "id": "sg:person.010167215443.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010167215443.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sarker", 
            "givenName": "Azmal", 
            "id": "sg:person.01327360443.04", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01327360443.04"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of 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 Hospital, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.412484.f", 
              "name": [
                "Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of 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": "Research Institute for Convergence Science, Seoul National University, 08826, Seoul, Republic of Korea", 
              "id": "http://www.grid.ac/institutes/grid.31501.36", 
              "name": [
                "Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea", 
                "Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea", 
                "Cancer Research Institute, Seoul National University, 03080, Seoul, Republic of Korea", 
                "Research Institute for Convergence Science, Seoul National University, 08826, Seoul, Republic of Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Im", 
            "givenName": "Hyung-Jun", 
            "id": "sg:person.01310744444.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01310744444.16"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00259-019-04500-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1120936317", 
              "https://doi.org/10.1007/s00259-019-04500-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-18489-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100172371", 
              "https://doi.org/10.1038/s41598-017-18489-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-016-3425-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004829889", 
              "https://doi.org/10.1007/s00259-016-3425-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-019-50079-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1121007705", 
              "https://doi.org/10.1038/s41598-019-50079-2"
            ], 
            "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.10.1186/s12885-020-6579-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124469417", 
              "https://doi.org/10.1186/s12885-020-6579-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41591-018-0045-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105071631", 
              "https://doi.org/10.1038/s41591-018-0045-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature06309", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000116337", 
              "https://doi.org/10.1038/nature06309"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-018-37186-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111102200", 
              "https://doi.org/10.1038/s41598-018-37186-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12885-018-4881-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107528824", 
              "https://doi.org/10.1186/s12885-018-4881-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10673", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044278169", 
              "https://doi.org/10.1038/nature10673"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1245/s10434-018-6506-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1104022722", 
              "https://doi.org/10.1245/s10434-018-6506-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13550-019-0563-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122797933", 
              "https://doi.org/10.1186/s13550-019-0563-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature13385", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038580641", 
              "https://doi.org/10.1038/nature13385"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sdata.2018.202", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107646053", 
              "https://doi.org/10.1038/sdata.2018.202"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-06-27", 
        "datePublishedReg": "2022-06-27", 
        "description": "BackgroundHigh levels of 18F-fluorodeoxyglucose (18F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of 18F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.ResultsUsing an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In 18F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan\u2013Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower 18F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P\u2009=\u2009.001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P\u2009=\u2009.027). A high TLR (P\u2009=\u2009.01) score of 18F-FDG PET parameters and a high T follicular helper cell (TFH) score (P\u2009=\u2009.005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P\u2009=\u2009.024 and .047).ConclusionsA high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s13550-022-00908-9", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1045165", 
            "issn": [
              "2191-219X"
            ], 
            "name": "EJNMMI Research", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "12"
          }
        ], 
        "keywords": [
          "non-small cell lung cancer", 
          "immune cell score", 
          "cytolytic score", 
          "PET parameters", 
          "lung adenocarcinoma", 
          "better prognosis", 
          "worse prognosis", 
          "primary tumor", 
          "positron emission tomography parameters", 
          "higher CD8 T cells", 
          "maximum standardized uptake value ratio", 
          "Kaplan-Meier survival analysis", 
          "standardized uptake value ratio", 
          "Cox regression test", 
          "follicular helper cells", 
          "CD8 T cells", 
          "cell lung cancer", 
          "immune cell infiltration", 
          "log-rank test", 
          "lung adenocarcinoma patients", 
          "uptake value ratio", 
          "chi-square test", 
          "BackgroundHigh levels", 
          "CYT score", 
          "overall survival", 
          "prognostic impact", 
          "clinicopathologic features", 
          "prognostic value", 
          "inflammatory group", 
          "adenocarcinoma patients", 
          "helper cells", 
          "lung cancer", 
          "cell infiltration", 
          "tomography parameters", 
          "T cells", 
          "PET scans", 
          "prognosis", 
          "subtype groups", 
          "tumor uptake", 
          "survival analysis", 
          "patients", 
          "PI group", 
          "adenocarcinoma", 
          "tumors", 
          "RNA sequencing datasets", 
          "scores", 
          "Tfh", 
          "TLR", 
          "lower stage", 
          "more females", 
          "ANOVA test", 
          "regression test", 
          "positive correlation", 
          "group", 
          "value ratio", 
          "high levels", 
          "cells", 
          "cancer", 
          "integrative analysis", 
          "survival", 
          "infiltration", 
          "test", 
          "scans", 
          "correlation analysis", 
          "levels", 
          "females", 
          "cell score", 
          "genomic analysis", 
          "uptake", 
          "sequencing datasets", 
          "analysis", 
          "study", 
          "correlation", 
          "stage", 
          "unit group", 
          "ratio", 
          "impact", 
          "tendency", 
          "features", 
          "parameters", 
          "opposite tendency", 
          "values", 
          "integration", 
          "dataset"
        ], 
        "name": "Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma", 
        "pagination": "38", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1149003249"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13550-022-00908-9"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "35759068"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13550-022-00908-9", 
          "https://app.dimensions.ai/details/publication/pub.1149003249"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:50", 
        "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_946.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s13550-022-00908-9"
      }
    ]
     

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

    HOW TO GET THIS DATA PROGRAMMATICALLY:

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

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13550-022-00908-9'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13550-022-00908-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13550-022-00908-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13550-022-00908-9'


     

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

    238 TRIPLES      21 PREDICATES      124 URIs      101 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13550-022-00908-9 schema:about anzsrc-for:11
    2 anzsrc-for:1107
    3 schema:author Nc618bd62047d4023bac48177db9c0653
    4 schema:citation sg:pub.10.1007/s00259-014-2903-7
    5 sg:pub.10.1007/s00259-016-3425-2
    6 sg:pub.10.1007/s00259-019-04500-7
    7 sg:pub.10.1038/nature06309
    8 sg:pub.10.1038/nature10673
    9 sg:pub.10.1038/nature13385
    10 sg:pub.10.1038/s41591-018-0045-3
    11 sg:pub.10.1038/s41598-017-18489-2
    12 sg:pub.10.1038/s41598-018-37186-2
    13 sg:pub.10.1038/s41598-019-50079-2
    14 sg:pub.10.1038/sdata.2018.202
    15 sg:pub.10.1186/s12885-018-4881-9
    16 sg:pub.10.1186/s12885-020-6579-z
    17 sg:pub.10.1186/s13550-019-0563-0
    18 sg:pub.10.1245/s10434-018-6506-6
    19 schema:datePublished 2022-06-27
    20 schema:datePublishedReg 2022-06-27
    21 schema:description BackgroundHigh levels of 18F-fluorodeoxyglucose (18F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of 18F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.ResultsUsing an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In 18F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower 18F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of 18F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).ConclusionsA high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.
    22 schema:genre article
    23 schema:isAccessibleForFree true
    24 schema:isPartOf Nace69986e11b42f3aa233a883e43bf77
    25 Nb3344bcc97974d5881b9e27f244fc0b7
    26 sg:journal.1045165
    27 schema:keywords ANOVA test
    28 BackgroundHigh levels
    29 CD8 T cells
    30 CYT score
    31 Cox regression test
    32 Kaplan-Meier survival analysis
    33 PET parameters
    34 PET scans
    35 PI group
    36 RNA sequencing datasets
    37 T cells
    38 TLR
    39 Tfh
    40 adenocarcinoma
    41 adenocarcinoma patients
    42 analysis
    43 better prognosis
    44 cancer
    45 cell infiltration
    46 cell lung cancer
    47 cell score
    48 cells
    49 chi-square test
    50 clinicopathologic features
    51 correlation
    52 correlation analysis
    53 cytolytic score
    54 dataset
    55 features
    56 females
    57 follicular helper cells
    58 genomic analysis
    59 group
    60 helper cells
    61 high levels
    62 higher CD8 T cells
    63 immune cell infiltration
    64 immune cell score
    65 impact
    66 infiltration
    67 inflammatory group
    68 integration
    69 integrative analysis
    70 levels
    71 log-rank test
    72 lower stage
    73 lung adenocarcinoma
    74 lung adenocarcinoma patients
    75 lung cancer
    76 maximum standardized uptake value ratio
    77 more females
    78 non-small cell lung cancer
    79 opposite tendency
    80 overall survival
    81 parameters
    82 patients
    83 positive correlation
    84 positron emission tomography parameters
    85 primary tumor
    86 prognosis
    87 prognostic impact
    88 prognostic value
    89 ratio
    90 regression test
    91 scans
    92 scores
    93 sequencing datasets
    94 stage
    95 standardized uptake value ratio
    96 study
    97 subtype groups
    98 survival
    99 survival analysis
    100 tendency
    101 test
    102 tomography parameters
    103 tumor uptake
    104 tumors
    105 unit group
    106 uptake
    107 uptake value ratio
    108 value ratio
    109 values
    110 worse prognosis
    111 schema:name Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma
    112 schema:pagination 38
    113 schema:productId N036b9f3ad7944581ad45ae155b796fe6
    114 N877a336c31d64f46b574ed898b8cf53b
    115 Ne954c034a8dc474c9f9f9054035d5110
    116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149003249
    117 https://doi.org/10.1186/s13550-022-00908-9
    118 schema:sdDatePublished 2022-10-01T06:50
    119 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    120 schema:sdPublisher N885fe2490f6a4887936892f9bb2c7f12
    121 schema:url https://doi.org/10.1186/s13550-022-00908-9
    122 sgo:license sg:explorer/license/
    123 sgo:sdDataset articles
    124 rdf:type schema:ScholarlyArticle
    125 N036b9f3ad7944581ad45ae155b796fe6 schema:name dimensions_id
    126 schema:value pub.1149003249
    127 rdf:type schema:PropertyValue
    128 N215e681abaae42a881f7d8f3fafa6821 rdf:first sg:person.015617314175.88
    129 rdf:rest Ne719bd4d3a8145de86efc1415d8368e1
    130 N4634cb8c717344e6905dee26089d1f22 rdf:first sg:person.01327360443.04
    131 rdf:rest N5b912dc9177b4dc282f7858fc6a2054e
    132 N5b912dc9177b4dc282f7858fc6a2054e rdf:first sg:person.0631257534.28
    133 rdf:rest N215e681abaae42a881f7d8f3fafa6821
    134 N877a336c31d64f46b574ed898b8cf53b schema:name pubmed_id
    135 schema:value 35759068
    136 rdf:type schema:PropertyValue
    137 N885fe2490f6a4887936892f9bb2c7f12 schema:name Springer Nature - SN SciGraph project
    138 rdf:type schema:Organization
    139 Nace69986e11b42f3aa233a883e43bf77 schema:volumeNumber 12
    140 rdf:type schema:PublicationVolume
    141 Nb3344bcc97974d5881b9e27f244fc0b7 schema:issueNumber 1
    142 rdf:type schema:PublicationIssue
    143 Nc618bd62047d4023bac48177db9c0653 rdf:first sg:person.010167215443.51
    144 rdf:rest N4634cb8c717344e6905dee26089d1f22
    145 Ne719bd4d3a8145de86efc1415d8368e1 rdf:first sg:person.01310744444.16
    146 rdf:rest rdf:nil
    147 Ne954c034a8dc474c9f9f9054035d5110 schema:name doi
    148 schema:value 10.1186/s13550-022-00908-9
    149 rdf:type schema:PropertyValue
    150 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    151 schema:name Medical and Health Sciences
    152 rdf:type schema:DefinedTerm
    153 anzsrc-for:1107 schema:inDefinedTermSet anzsrc-for:
    154 schema:name Immunology
    155 rdf:type schema:DefinedTerm
    156 sg:journal.1045165 schema:issn 2191-219X
    157 schema:name EJNMMI Research
    158 rdf:type schema:Periodical
    159 sg:person.010167215443.51 schema:affiliation grid-institutes:grid.31501.36
    160 schema:familyName Choi
    161 schema:givenName Jinyeong
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010167215443.51
    163 rdf:type schema:Person
    164 sg:person.01310744444.16 schema:affiliation grid-institutes:grid.31501.36
    165 schema:familyName Im
    166 schema:givenName Hyung-Jun
    167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01310744444.16
    168 rdf:type schema:Person
    169 sg:person.01327360443.04 schema:affiliation grid-institutes:grid.412484.f
    170 schema:familyName Sarker
    171 schema:givenName Azmal
    172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01327360443.04
    173 rdf:type schema:Person
    174 sg:person.015617314175.88 schema:affiliation grid-institutes:grid.412484.f
    175 schema:familyName Lee
    176 schema:givenName Dong Soo
    177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015617314175.88
    178 rdf:type schema:Person
    179 sg:person.0631257534.28 schema:affiliation grid-institutes:grid.412484.f
    180 schema:familyName Choi
    181 schema:givenName Hongyoon
    182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0631257534.28
    183 rdf:type schema:Person
    184 sg:pub.10.1007/s00259-014-2903-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007455399
    185 https://doi.org/10.1007/s00259-014-2903-7
    186 rdf:type schema:CreativeWork
    187 sg:pub.10.1007/s00259-016-3425-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004829889
    188 https://doi.org/10.1007/s00259-016-3425-2
    189 rdf:type schema:CreativeWork
    190 sg:pub.10.1007/s00259-019-04500-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1120936317
    191 https://doi.org/10.1007/s00259-019-04500-7
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1038/nature06309 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000116337
    194 https://doi.org/10.1038/nature06309
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1038/nature10673 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044278169
    197 https://doi.org/10.1038/nature10673
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1038/nature13385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038580641
    200 https://doi.org/10.1038/nature13385
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1038/s41591-018-0045-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105071631
    203 https://doi.org/10.1038/s41591-018-0045-3
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1038/s41598-017-18489-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100172371
    206 https://doi.org/10.1038/s41598-017-18489-2
    207 rdf:type schema:CreativeWork
    208 sg:pub.10.1038/s41598-018-37186-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111102200
    209 https://doi.org/10.1038/s41598-018-37186-2
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1038/s41598-019-50079-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1121007705
    212 https://doi.org/10.1038/s41598-019-50079-2
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1038/sdata.2018.202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107646053
    215 https://doi.org/10.1038/sdata.2018.202
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1186/s12885-018-4881-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107528824
    218 https://doi.org/10.1186/s12885-018-4881-9
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1186/s12885-020-6579-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1124469417
    221 https://doi.org/10.1186/s12885-020-6579-z
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1186/s13550-019-0563-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122797933
    224 https://doi.org/10.1186/s13550-019-0563-0
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1245/s10434-018-6506-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104022722
    227 https://doi.org/10.1245/s10434-018-6506-6
    228 rdf:type schema:CreativeWork
    229 grid-institutes:grid.31501.36 schema:alternateName Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea
    230 Research Institute for Convergence Science, Seoul National University, 08826, Seoul, Republic of Korea
    231 schema:name Cancer Research Institute, Seoul National University, 03080, Seoul, Republic of Korea
    232 Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea
    233 Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, 08826, Seoul, Republic of Korea
    234 Research Institute for Convergence Science, Seoul National University, 08826, Seoul, Republic of Korea
    235 rdf:type schema:Organization
    236 grid-institutes:grid.412484.f schema:alternateName Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    237 schema:name Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
    238 rdf:type schema:Organization
     




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


    ...