[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-03-26

AUTHORS

Marta Ferreira, Pierre Lovinfosse, Johanne Hermesse, Marjolein Decuypere, Caroline Rousseau, François Lucia, Ulrike Schick, Caroline Reinhold, Philippe Robin, Mathieu Hatt, Dimitris Visvikis, Claire Bernard, Ralph T. H. Leijenaar, Frédéric Kridelka, Philippe Lambin, Patrick E. Meyer, Roland Hustinx

ABSTRACT

PurposeTo test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).MethodsOne hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners.ResultsAfter a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67–0.88), 0.49 (0.25–0.67), 0.42 (0.25–0.60) and 0.63 (0.20–0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set.Conclusion[18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient’s outcome but remain subject to variability across PET/CT devices. More... »

PAGES

3432-3443

References to SciGraph publications

  • 2018-05-04. The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology Guidelines for the Management of Patients with Cervical Cancer in VIRCHOWS ARCHIV
  • 2015-08-17. Machine Learning methods for Quantitative Radiomic Biomarkers in SCIENTIFIC REPORTS
  • 2019-05-06. Deep learning-based survival prediction of oral cancer patients in SCIENTIFIC REPORTS
  • 2019-11-06. Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2020-06-24. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies in SCIENTIFIC REPORTS
  • 2014-06-03. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach in NATURE COMMUNICATIONS
  • 2018-10-05. Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis in RADIATION ONCOLOGY
  • 2019-06-18. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2017-10-04. Radiomics: the bridge between medical imaging and personalized medicine in NATURE REVIEWS CLINICAL ONCOLOGY
  • 2020-01-15. Impact of contouring variability on oncological PET radiomics features in the lung in SCIENTIFIC REPORTS
  • 2019-05-27. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography in EUROPEAN RADIOLOGY
  • 2017-12-09. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-08-05. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis in SCIENTIFIC REPORTS
  • 2018-05-23. Correction to: The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology Guidelines for the Management of Patients with Cervical Cancer in VIRCHOWS ARCHIV
  • 2018-12-07. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2017-10-16. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling in SCIENTIFIC REPORTS
  • 2019-02-07. Chemoradiotherapy for locally advanced cervix cancer without aortic lymph node involvement: can we consider metabolic parameters of pretherapeutic FDG-PET/CT for treatment tailoring? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2019-07-04. Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging in SCIENTIFIC REPORTS
  • 2013. Feature Selection Based on Fuzzy Mutual Information in FUZZY LOGIC AND APPLICATIONS
  • 2017-05-31. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies in EUROPEAN RADIOLOGY
  • 2015-01-06. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement in BMC MEDICINE
  • 2017-08-16. 18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer in ANNALS OF NUCLEAR MEDICINE
  • 2018-07-25. FDG PET radiomics: a review of the methodological aspects in CLINICAL AND TRANSLATIONAL IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00259-021-05303-5

    DOI

    http://dx.doi.org/10.1007/s00259-021-05303-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1136694817

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/33772334


    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/2008/1112", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Oncology and Carcinogenesis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Bayes Theorem", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Disease-Free Survival", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Fluorodeoxyglucose F18", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neoplasm Recurrence, Local", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Positron Emission Tomography Computed Tomography", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Radiopharmaceuticals", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Retrospective Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Uterine Cervical Neoplasms", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "GIGA-CRC in vivo Imaging, University of Li\u00e8ge, GIGA, Avenue de l\u2019H\u00f4pital 11, 4000, Liege, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.4861.b", 
              "name": [
                "GIGA-CRC in vivo Imaging, University of Li\u00e8ge, GIGA, Avenue de l\u2019H\u00f4pital 11, 4000, Liege, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ferreira", 
            "givenName": "Marta", 
            "id": "sg:person.014017400423.66", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014017400423.66"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Nuclear Medicine and Oncological Imaging, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.411374.4", 
              "name": [
                "Division of Nuclear Medicine and Oncological Imaging, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lovinfosse", 
            "givenName": "Pierre", 
            "id": "sg:person.01047474242.88", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047474242.88"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiation Oncology, Li\u00e8ge University Hospital, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.411374.4", 
              "name": [
                "Department of Radiation Oncology, Li\u00e8ge University Hospital, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hermesse", 
            "givenName": "Johanne", 
            "id": "sg:person.01042314324.67", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042314324.67"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Oncological Gynecology, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.411374.4", 
              "name": [
                "Division of Oncological Gynecology, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Decuypere", 
            "givenName": "Marjolein", 
            "id": "sg:person.010021241263.42", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010021241263.42"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "ICO Ren\u00e9 Gauducheau, F-44800, Saint-Herblain, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Universit\u00e9 de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France", 
                "ICO Ren\u00e9 Gauducheau, F-44800, Saint-Herblain, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rousseau", 
            "givenName": "Caroline", 
            "id": "sg:person.01240530667.47", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240530667.47"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.6289.5", 
              "name": [
                "Radiation Oncology Department, University Hospital, Brest, France", 
                "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lucia", 
            "givenName": "Fran\u00e7ois", 
            "id": "sg:person.07532416715.90", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07532416715.90"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.6289.5", 
              "name": [
                "Radiation Oncology Department, University Hospital, Brest, France", 
                "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Schick", 
            "givenName": "Ulrike", 
            "id": "sg:person.01206441206.87", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01206441206.87"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada", 
              "id": "http://www.grid.ac/institutes/grid.63984.30", 
              "name": [
                "Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Reinhold", 
            "givenName": "Caroline", 
            "id": "sg:person.01134767703.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134767703.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Nuclear Medicine and EA3878, Brest University Hospital, University of Brest, Brest, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Department of Nuclear Medicine and EA3878, Brest University Hospital, University of Brest, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Robin", 
            "givenName": "Philippe", 
            "id": "sg:person.01307140060.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307140060.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.6289.5", 
              "name": [
                "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hatt", 
            "givenName": "Mathieu", 
            "id": "sg:person.01202724075.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202724075.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.6289.5", 
              "name": [
                "LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Visvikis", 
            "givenName": "Dimitris", 
            "id": "sg:person.01255045106.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255045106.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Nuclear Medicine and Oncological Imaging, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.411374.4", 
              "name": [
                "Division of Nuclear Medicine and Oncological Imaging, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bernard", 
            "givenName": "Claire", 
            "id": "sg:person.01107133011.03", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107133011.03"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "The-D Lab, Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.412966.e", 
              "name": [
                "Oncoradiomics SA, Clos Chanmurly 13, 4000, Li\u00e8ge, Belgium", 
                "The-D Lab, Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Leijenaar", 
            "givenName": "Ralph T. H.", 
            "id": "sg:person.01054555705.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054555705.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Oncological Gynecology, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.411374.4", 
              "name": [
                "Division of Oncological Gynecology, University Hospital of Li\u00e8ge, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kridelka", 
            "givenName": "Fr\u00e9d\u00e9ric", 
            "id": "sg:person.01101734510.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101734510.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.412966.e", 
              "name": [
                "The-D Lab, Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands", 
                "Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lambin", 
            "givenName": "Philippe", 
            "id": "sg:person.0763075314.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763075314.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Bioinformatics and Systems Biology Lab, University of Li\u00e8ge, Li\u00e8ge, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.4861.b", 
              "name": [
                "Bioinformatics and Systems Biology Lab, University of Li\u00e8ge, Li\u00e8ge, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Meyer", 
            "givenName": "Patrick E.", 
            "id": "sg:person.012326011263.01", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012326011263.01"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "GIGA-CRC in vivo Imaging, University of Li\u00e8ge, GIGA, Avenue de l\u2019H\u00f4pital 11, 4000, Liege, Belgium", 
              "id": "http://www.grid.ac/institutes/grid.4861.b", 
              "name": [
                "GIGA-CRC in vivo Imaging, University of Li\u00e8ge, GIGA, Avenue de l\u2019H\u00f4pital 11, 4000, Liege, Belgium"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hustinx", 
            "givenName": "Roland", 
            "id": "sg:person.01326411430.39", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326411430.39"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/srep13087", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036587522", 
              "https://doi.org/10.1038/srep13087"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-017-3898-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099696659", 
              "https://doi.org/10.1007/s00259-017-3898-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12916-014-0241-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023303896", 
              "https://doi.org/10.1186/s12916-014-0241-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-019-43372-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1113943773", 
              "https://doi.org/10.1038/s41598-019-43372-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms5006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009469125", 
              "https://doi.org/10.1038/ncomms5006"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-03200-9_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019531403", 
              "https://doi.org/10.1007/978-3-319-03200-9_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-019-46030-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1117740067", 
              "https://doi.org/10.1038/s41598-019-46030-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00428-018-2362-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103793073", 
              "https://doi.org/10.1007/s00428-018-2362-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-018-4219-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111949171", 
              "https://doi.org/10.1007/s00259-018-4219-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00330-017-4859-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085731538", 
              "https://doi.org/10.1007/s00330-017-4859-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12149-017-1199-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091220083", 
              "https://doi.org/10.1007/s12149-017-1199-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrclinonc.2017.141", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092061102", 
              "https://doi.org/10.1038/nrclinonc.2017.141"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00428-018-2380-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1104141633", 
              "https://doi.org/10.1007/s00428-018-2380-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-018-4231-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110448603", 
              "https://doi.org/10.1007/s00259-018-4231-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-019-04372-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1117291124", 
              "https://doi.org/10.1007/s00259-019-04372-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-020-66110-w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128717081", 
              "https://doi.org/10.1038/s41598-020-66110-w"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-13448-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092152818", 
              "https://doi.org/10.1038/s41598-017-13448-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep11075", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038280199", 
              "https://doi.org/10.1038/srep11075"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-019-57171-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1124096736", 
              "https://doi.org/10.1038/s41598-019-57171-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40336-018-0292-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105830889", 
              "https://doi.org/10.1007/s40336-018-0292-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13014-018-1140-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107432580", 
              "https://doi.org/10.1186/s13014-018-1140-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00330-019-06265-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1115907618", 
              "https://doi.org/10.1007/s00330-019-06265-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-019-04493-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122362302", 
              "https://doi.org/10.1007/s00259-019-04493-3"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-03-26", 
        "datePublishedReg": "2021-03-26", 
        "description": "PurposeTo test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).MethodsOne hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners.ResultsAfter a median follow-up of 23\u00a0months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67\u20130.88), 0.49 (0.25\u20130.67), 0.42 (0.25\u20130.60) and 0.63 (0.20\u20130.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set.Conclusion[18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient\u2019s outcome but remain subject to variability across PET/CT devices.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00259-021-05303-5", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.7070033", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1297401", 
            "issn": [
              "1619-7070", 
              "1619-7089"
            ], 
            "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "11", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "48"
          }
        ], 
        "keywords": [
          "radiomic features", 
          "cervical cancer", 
          "cancer recurrence", 
          "D-glucose PET/CT", 
          "disease-free survival", 
          "advanced cervical cancer", 
          "standard clinical parameters", 
          "proportional hazards model", 
          "PET/CT", 
          "MethodsOne hundred fifty", 
          "PET radiomic features", 
          "clinical features", 
          "clinical parameters", 
          "center study", 
          "hundred fifty", 
          "patient outcomes", 
          "clinical data", 
          "clinical information", 
          "hazards model", 
          "normal liver", 
          "patients", 
          "tumors", 
          "multiple centers", 
          "statistical significance", 
          "PET/CT devices", 
          "PET radiomics", 
          "external validation", 
          "recurrence", 
          "cancer", 
          "effects of combat", 
          "outcomes", 
          "TLR", 
          "CT devices", 
          "PurposeTo", 
          "ResultsAfter", 
          "adaptive Bayesian algorithm", 
          "liver", 
          "LACC", 
          "months", 
          "CT", 
          "survival", 
          "radiomics", 
          "predictive performance", 
          "study", 
          "scanner", 
          "best model", 
          "fifties", 
          "fivefold cross validation", 
          "features", 
          "relevant information", 
          "data", 
          "significance", 
          "center", 
          "combat", 
          "effect", 
          "validation", 
          "recall", 
          "information", 
          "reproducibility", 
          "model", 
          "number", 
          "curves", 
          "cross validation", 
          "variability", 
          "area", 
          "values", 
          "region", 
          "number of features", 
          "method", 
          "test set", 
          "harmonisation", 
          "different feature selection methods", 
          "parameters", 
          "model performance", 
          "terms", 
          "machine learning", 
          "performance", 
          "devices", 
          "learning", 
          "precision", 
          "feature selection method", 
          "Bayesian algorithm", 
          "set", 
          "selection method", 
          "classifier", 
          "algorithm"
        ], 
        "name": "[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation", 
        "pagination": "3432-3443", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1136694817"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00259-021-05303-5"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "33772334"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00259-021-05303-5", 
          "https://app.dimensions.ai/details/publication/pub.1136694817"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:48", 
        "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_885.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00259-021-05303-5"
      }
    ]
     

    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/s00259-021-05303-5'

    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/s00259-021-05303-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00259-021-05303-5'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00259-021-05303-5'


     

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

    425 TRIPLES      21 PREDICATES      145 URIs      114 LITERALS      18 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00259-021-05303-5 schema:about N0c933bb763ac49d9ba780b93982b2be1
    2 N0f81cc3df6f44eada97549af75f58bd5
    3 N12053b91fef34ebe9c773f9201bc2f45
    4 N176fc4ee060444d1bd2d95a28d828c92
    5 N73447d2f618943a6865b65c90330bbf1
    6 N7634b07ab7c74c47ba6b8025edefe7ef
    7 N889d5cd67ce6414e9bbd841f7081a3b5
    8 Nb3e19f25104d476a8efce35c8c740f28
    9 Nc6d8dbb6aef842c387ef9988390fe023
    10 Ndeb1998a46564068830a2e80f1fd808d
    11 Ne731f7ccdf3645809ed2726ac3d732bd
    12 anzsrc-for:11
    13 anzsrc-for:1112
    14 schema:author N822d404666da466dbf63889a0233dbde
    15 schema:citation sg:pub.10.1007/978-3-319-03200-9_4
    16 sg:pub.10.1007/s00259-017-3898-7
    17 sg:pub.10.1007/s00259-018-4219-5
    18 sg:pub.10.1007/s00259-018-4231-9
    19 sg:pub.10.1007/s00259-019-04372-x
    20 sg:pub.10.1007/s00259-019-04493-3
    21 sg:pub.10.1007/s00330-017-4859-z
    22 sg:pub.10.1007/s00330-019-06265-x
    23 sg:pub.10.1007/s00428-018-2362-9
    24 sg:pub.10.1007/s00428-018-2380-7
    25 sg:pub.10.1007/s12149-017-1199-7
    26 sg:pub.10.1007/s40336-018-0292-9
    27 sg:pub.10.1038/ncomms5006
    28 sg:pub.10.1038/nrclinonc.2017.141
    29 sg:pub.10.1038/s41598-017-13448-3
    30 sg:pub.10.1038/s41598-019-43372-7
    31 sg:pub.10.1038/s41598-019-46030-0
    32 sg:pub.10.1038/s41598-019-57171-7
    33 sg:pub.10.1038/s41598-020-66110-w
    34 sg:pub.10.1038/srep11075
    35 sg:pub.10.1038/srep13087
    36 sg:pub.10.1186/s12916-014-0241-z
    37 sg:pub.10.1186/s13014-018-1140-9
    38 schema:datePublished 2021-03-26
    39 schema:datePublishedReg 2021-03-26
    40 schema:description PurposeTo test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).MethodsOne hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners.ResultsAfter a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67–0.88), 0.49 (0.25–0.67), 0.42 (0.25–0.60) and 0.63 (0.20–0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set.Conclusion[18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient’s outcome but remain subject to variability across PET/CT devices.
    41 schema:genre article
    42 schema:isAccessibleForFree true
    43 schema:isPartOf N731e084e643144199eb70bde3f1d63ea
    44 N9c84e2d55cd44a0b8d1f53946f482b47
    45 sg:journal.1297401
    46 schema:keywords Bayesian algorithm
    47 CT
    48 CT devices
    49 D-glucose PET/CT
    50 LACC
    51 MethodsOne hundred fifty
    52 PET radiomic features
    53 PET radiomics
    54 PET/CT
    55 PET/CT devices
    56 PurposeTo
    57 ResultsAfter
    58 TLR
    59 adaptive Bayesian algorithm
    60 advanced cervical cancer
    61 algorithm
    62 area
    63 best model
    64 cancer
    65 cancer recurrence
    66 center
    67 center study
    68 cervical cancer
    69 classifier
    70 clinical data
    71 clinical features
    72 clinical information
    73 clinical parameters
    74 combat
    75 cross validation
    76 curves
    77 data
    78 devices
    79 different feature selection methods
    80 disease-free survival
    81 effect
    82 effects of combat
    83 external validation
    84 feature selection method
    85 features
    86 fifties
    87 fivefold cross validation
    88 harmonisation
    89 hazards model
    90 hundred fifty
    91 information
    92 learning
    93 liver
    94 machine learning
    95 method
    96 model
    97 model performance
    98 months
    99 multiple centers
    100 normal liver
    101 number
    102 number of features
    103 outcomes
    104 parameters
    105 patient outcomes
    106 patients
    107 performance
    108 precision
    109 predictive performance
    110 proportional hazards model
    111 radiomic features
    112 radiomics
    113 recall
    114 recurrence
    115 region
    116 relevant information
    117 reproducibility
    118 scanner
    119 selection method
    120 set
    121 significance
    122 standard clinical parameters
    123 statistical significance
    124 study
    125 survival
    126 terms
    127 test set
    128 tumors
    129 validation
    130 values
    131 variability
    132 schema:name [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation
    133 schema:pagination 3432-3443
    134 schema:productId N8a1d078f2aa0402db78e8ed7f1cd897a
    135 Na1d7cd39b4a545f6b09ba7802b7be77f
    136 Na94cb1430fb947258efa34a4c2bf3884
    137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1136694817
    138 https://doi.org/10.1007/s00259-021-05303-5
    139 schema:sdDatePublished 2022-10-01T06:48
    140 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    141 schema:sdPublisher Nb139634c47d548ca82586cd842ff2b8f
    142 schema:url https://doi.org/10.1007/s00259-021-05303-5
    143 sgo:license sg:explorer/license/
    144 sgo:sdDataset articles
    145 rdf:type schema:ScholarlyArticle
    146 N01549a7f757e4a96b4e7a056d439bc33 rdf:first sg:person.012326011263.01
    147 rdf:rest Nd123083b533144679c9fbe485136b717
    148 N05278a8227c24736b2d3b66a00a11b65 rdf:first sg:person.01042314324.67
    149 rdf:rest N5087e0b4aee54ed494a14ac22cfcc04e
    150 N0ba08e2f5ee14f57a6fce8e3eb4b1b53 rdf:first sg:person.01206441206.87
    151 rdf:rest N12e2d1eb568a4cbd8adf30fa64b28890
    152 N0c933bb763ac49d9ba780b93982b2be1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    153 schema:name Neoplasm Recurrence, Local
    154 rdf:type schema:DefinedTerm
    155 N0f81cc3df6f44eada97549af75f58bd5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    156 schema:name Positron Emission Tomography Computed Tomography
    157 rdf:type schema:DefinedTerm
    158 N12053b91fef34ebe9c773f9201bc2f45 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    159 schema:name Uterine Cervical Neoplasms
    160 rdf:type schema:DefinedTerm
    161 N12e2d1eb568a4cbd8adf30fa64b28890 rdf:first sg:person.01134767703.78
    162 rdf:rest Na17f11e4c07d4425979dbd2dde916138
    163 N176fc4ee060444d1bd2d95a28d828c92 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    164 schema:name Reproducibility of Results
    165 rdf:type schema:DefinedTerm
    166 N2023f1b4ef4a4cf48db366f84f5e0045 rdf:first sg:person.01107133011.03
    167 rdf:rest Nd391bbeaf0774b56bee5e67bfff59f48
    168 N5087e0b4aee54ed494a14ac22cfcc04e rdf:first sg:person.010021241263.42
    169 rdf:rest Nf30449fd690542a49a1cb3f6d2fb67d5
    170 N5f7dee2217454fa9b63c04c15e3ce51f rdf:first sg:person.01047474242.88
    171 rdf:rest N05278a8227c24736b2d3b66a00a11b65
    172 N61ae056d1dd24a9aa2e1dd0040d119aa rdf:first sg:person.07532416715.90
    173 rdf:rest N0ba08e2f5ee14f57a6fce8e3eb4b1b53
    174 N731e084e643144199eb70bde3f1d63ea schema:issueNumber 11
    175 rdf:type schema:PublicationIssue
    176 N73447d2f618943a6865b65c90330bbf1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    177 schema:name Female
    178 rdf:type schema:DefinedTerm
    179 N7634b07ab7c74c47ba6b8025edefe7ef schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    180 schema:name Bayes Theorem
    181 rdf:type schema:DefinedTerm
    182 N822d404666da466dbf63889a0233dbde rdf:first sg:person.014017400423.66
    183 rdf:rest N5f7dee2217454fa9b63c04c15e3ce51f
    184 N889d5cd67ce6414e9bbd841f7081a3b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    185 schema:name Humans
    186 rdf:type schema:DefinedTerm
    187 N88f7152ed2ec449a86a7bff1e0650231 rdf:first sg:person.0763075314.50
    188 rdf:rest N01549a7f757e4a96b4e7a056d439bc33
    189 N8a1d078f2aa0402db78e8ed7f1cd897a schema:name pubmed_id
    190 schema:value 33772334
    191 rdf:type schema:PropertyValue
    192 N9892f5b7db71434e9a68fed187be8b66 rdf:first sg:person.01255045106.49
    193 rdf:rest N2023f1b4ef4a4cf48db366f84f5e0045
    194 N9c84e2d55cd44a0b8d1f53946f482b47 schema:volumeNumber 48
    195 rdf:type schema:PublicationVolume
    196 Na17f11e4c07d4425979dbd2dde916138 rdf:first sg:person.01307140060.86
    197 rdf:rest Na21e639109f44381a997169b4df1b206
    198 Na1d7cd39b4a545f6b09ba7802b7be77f schema:name dimensions_id
    199 schema:value pub.1136694817
    200 rdf:type schema:PropertyValue
    201 Na21e639109f44381a997169b4df1b206 rdf:first sg:person.01202724075.78
    202 rdf:rest N9892f5b7db71434e9a68fed187be8b66
    203 Na94cb1430fb947258efa34a4c2bf3884 schema:name doi
    204 schema:value 10.1007/s00259-021-05303-5
    205 rdf:type schema:PropertyValue
    206 Nb139634c47d548ca82586cd842ff2b8f schema:name Springer Nature - SN SciGraph project
    207 rdf:type schema:Organization
    208 Nb3e19f25104d476a8efce35c8c740f28 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    209 schema:name Disease-Free Survival
    210 rdf:type schema:DefinedTerm
    211 Nc6d8dbb6aef842c387ef9988390fe023 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    212 schema:name Fluorodeoxyglucose F18
    213 rdf:type schema:DefinedTerm
    214 Nd123083b533144679c9fbe485136b717 rdf:first sg:person.01326411430.39
    215 rdf:rest rdf:nil
    216 Nd391bbeaf0774b56bee5e67bfff59f48 rdf:first sg:person.01054555705.34
    217 rdf:rest Ne2c046a4b3fb4c4791e8ef1989eb354d
    218 Ndeb1998a46564068830a2e80f1fd808d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    219 schema:name Retrospective Studies
    220 rdf:type schema:DefinedTerm
    221 Ne2c046a4b3fb4c4791e8ef1989eb354d rdf:first sg:person.01101734510.44
    222 rdf:rest N88f7152ed2ec449a86a7bff1e0650231
    223 Ne731f7ccdf3645809ed2726ac3d732bd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    224 schema:name Radiopharmaceuticals
    225 rdf:type schema:DefinedTerm
    226 Nf30449fd690542a49a1cb3f6d2fb67d5 rdf:first sg:person.01240530667.47
    227 rdf:rest N61ae056d1dd24a9aa2e1dd0040d119aa
    228 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    229 schema:name Medical and Health Sciences
    230 rdf:type schema:DefinedTerm
    231 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    232 schema:name Oncology and Carcinogenesis
    233 rdf:type schema:DefinedTerm
    234 sg:grant.7070033 http://pending.schema.org/fundedItem sg:pub.10.1007/s00259-021-05303-5
    235 rdf:type schema:MonetaryGrant
    236 sg:journal.1297401 schema:issn 1619-7070
    237 1619-7089
    238 schema:name European Journal of Nuclear Medicine and Molecular Imaging
    239 schema:publisher Springer Nature
    240 rdf:type schema:Periodical
    241 sg:person.010021241263.42 schema:affiliation grid-institutes:grid.411374.4
    242 schema:familyName Decuypere
    243 schema:givenName Marjolein
    244 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010021241263.42
    245 rdf:type schema:Person
    246 sg:person.01042314324.67 schema:affiliation grid-institutes:grid.411374.4
    247 schema:familyName Hermesse
    248 schema:givenName Johanne
    249 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042314324.67
    250 rdf:type schema:Person
    251 sg:person.01047474242.88 schema:affiliation grid-institutes:grid.411374.4
    252 schema:familyName Lovinfosse
    253 schema:givenName Pierre
    254 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047474242.88
    255 rdf:type schema:Person
    256 sg:person.01054555705.34 schema:affiliation grid-institutes:grid.412966.e
    257 schema:familyName Leijenaar
    258 schema:givenName Ralph T. H.
    259 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054555705.34
    260 rdf:type schema:Person
    261 sg:person.01101734510.44 schema:affiliation grid-institutes:grid.411374.4
    262 schema:familyName Kridelka
    263 schema:givenName Frédéric
    264 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101734510.44
    265 rdf:type schema:Person
    266 sg:person.01107133011.03 schema:affiliation grid-institutes:grid.411374.4
    267 schema:familyName Bernard
    268 schema:givenName Claire
    269 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01107133011.03
    270 rdf:type schema:Person
    271 sg:person.01134767703.78 schema:affiliation grid-institutes:grid.63984.30
    272 schema:familyName Reinhold
    273 schema:givenName Caroline
    274 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134767703.78
    275 rdf:type schema:Person
    276 sg:person.01202724075.78 schema:affiliation grid-institutes:grid.6289.5
    277 schema:familyName Hatt
    278 schema:givenName Mathieu
    279 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202724075.78
    280 rdf:type schema:Person
    281 sg:person.01206441206.87 schema:affiliation grid-institutes:grid.6289.5
    282 schema:familyName Schick
    283 schema:givenName Ulrike
    284 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01206441206.87
    285 rdf:type schema:Person
    286 sg:person.012326011263.01 schema:affiliation grid-institutes:grid.4861.b
    287 schema:familyName Meyer
    288 schema:givenName Patrick E.
    289 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012326011263.01
    290 rdf:type schema:Person
    291 sg:person.01240530667.47 schema:affiliation grid-institutes:None
    292 schema:familyName Rousseau
    293 schema:givenName Caroline
    294 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240530667.47
    295 rdf:type schema:Person
    296 sg:person.01255045106.49 schema:affiliation grid-institutes:grid.6289.5
    297 schema:familyName Visvikis
    298 schema:givenName Dimitris
    299 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255045106.49
    300 rdf:type schema:Person
    301 sg:person.01307140060.86 schema:affiliation grid-institutes:None
    302 schema:familyName Robin
    303 schema:givenName Philippe
    304 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307140060.86
    305 rdf:type schema:Person
    306 sg:person.01326411430.39 schema:affiliation grid-institutes:grid.4861.b
    307 schema:familyName Hustinx
    308 schema:givenName Roland
    309 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326411430.39
    310 rdf:type schema:Person
    311 sg:person.014017400423.66 schema:affiliation grid-institutes:grid.4861.b
    312 schema:familyName Ferreira
    313 schema:givenName Marta
    314 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014017400423.66
    315 rdf:type schema:Person
    316 sg:person.07532416715.90 schema:affiliation grid-institutes:grid.6289.5
    317 schema:familyName Lucia
    318 schema:givenName François
    319 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07532416715.90
    320 rdf:type schema:Person
    321 sg:person.0763075314.50 schema:affiliation grid-institutes:grid.412966.e
    322 schema:familyName Lambin
    323 schema:givenName Philippe
    324 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763075314.50
    325 rdf:type schema:Person
    326 sg:pub.10.1007/978-3-319-03200-9_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019531403
    327 https://doi.org/10.1007/978-3-319-03200-9_4
    328 rdf:type schema:CreativeWork
    329 sg:pub.10.1007/s00259-017-3898-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099696659
    330 https://doi.org/10.1007/s00259-017-3898-7
    331 rdf:type schema:CreativeWork
    332 sg:pub.10.1007/s00259-018-4219-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111949171
    333 https://doi.org/10.1007/s00259-018-4219-5
    334 rdf:type schema:CreativeWork
    335 sg:pub.10.1007/s00259-018-4231-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110448603
    336 https://doi.org/10.1007/s00259-018-4231-9
    337 rdf:type schema:CreativeWork
    338 sg:pub.10.1007/s00259-019-04372-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1117291124
    339 https://doi.org/10.1007/s00259-019-04372-x
    340 rdf:type schema:CreativeWork
    341 sg:pub.10.1007/s00259-019-04493-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122362302
    342 https://doi.org/10.1007/s00259-019-04493-3
    343 rdf:type schema:CreativeWork
    344 sg:pub.10.1007/s00330-017-4859-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1085731538
    345 https://doi.org/10.1007/s00330-017-4859-z
    346 rdf:type schema:CreativeWork
    347 sg:pub.10.1007/s00330-019-06265-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1115907618
    348 https://doi.org/10.1007/s00330-019-06265-x
    349 rdf:type schema:CreativeWork
    350 sg:pub.10.1007/s00428-018-2362-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103793073
    351 https://doi.org/10.1007/s00428-018-2362-9
    352 rdf:type schema:CreativeWork
    353 sg:pub.10.1007/s00428-018-2380-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104141633
    354 https://doi.org/10.1007/s00428-018-2380-7
    355 rdf:type schema:CreativeWork
    356 sg:pub.10.1007/s12149-017-1199-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091220083
    357 https://doi.org/10.1007/s12149-017-1199-7
    358 rdf:type schema:CreativeWork
    359 sg:pub.10.1007/s40336-018-0292-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105830889
    360 https://doi.org/10.1007/s40336-018-0292-9
    361 rdf:type schema:CreativeWork
    362 sg:pub.10.1038/ncomms5006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009469125
    363 https://doi.org/10.1038/ncomms5006
    364 rdf:type schema:CreativeWork
    365 sg:pub.10.1038/nrclinonc.2017.141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092061102
    366 https://doi.org/10.1038/nrclinonc.2017.141
    367 rdf:type schema:CreativeWork
    368 sg:pub.10.1038/s41598-017-13448-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092152818
    369 https://doi.org/10.1038/s41598-017-13448-3
    370 rdf:type schema:CreativeWork
    371 sg:pub.10.1038/s41598-019-43372-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113943773
    372 https://doi.org/10.1038/s41598-019-43372-7
    373 rdf:type schema:CreativeWork
    374 sg:pub.10.1038/s41598-019-46030-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1117740067
    375 https://doi.org/10.1038/s41598-019-46030-0
    376 rdf:type schema:CreativeWork
    377 sg:pub.10.1038/s41598-019-57171-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124096736
    378 https://doi.org/10.1038/s41598-019-57171-7
    379 rdf:type schema:CreativeWork
    380 sg:pub.10.1038/s41598-020-66110-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1128717081
    381 https://doi.org/10.1038/s41598-020-66110-w
    382 rdf:type schema:CreativeWork
    383 sg:pub.10.1038/srep11075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038280199
    384 https://doi.org/10.1038/srep11075
    385 rdf:type schema:CreativeWork
    386 sg:pub.10.1038/srep13087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036587522
    387 https://doi.org/10.1038/srep13087
    388 rdf:type schema:CreativeWork
    389 sg:pub.10.1186/s12916-014-0241-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1023303896
    390 https://doi.org/10.1186/s12916-014-0241-z
    391 rdf:type schema:CreativeWork
    392 sg:pub.10.1186/s13014-018-1140-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107432580
    393 https://doi.org/10.1186/s13014-018-1140-9
    394 rdf:type schema:CreativeWork
    395 grid-institutes:None schema:alternateName Department of Nuclear Medicine and EA3878, Brest University Hospital, University of Brest, Brest, France
    396 ICO René Gauducheau, F-44800, Saint-Herblain, France
    397 schema:name Department of Nuclear Medicine and EA3878, Brest University Hospital, University of Brest, Brest, France
    398 ICO René Gauducheau, F-44800, Saint-Herblain, France
    399 Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France
    400 rdf:type schema:Organization
    401 grid-institutes:grid.411374.4 schema:alternateName Department of Radiation Oncology, Liège University Hospital, Liège, Belgium
    402 Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
    403 Division of Oncological Gynecology, University Hospital of Liège, Liège, Belgium
    404 schema:name Department of Radiation Oncology, Liège University Hospital, Liège, Belgium
    405 Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
    406 Division of Oncological Gynecology, University Hospital of Liège, Liège, Belgium
    407 rdf:type schema:Organization
    408 grid-institutes:grid.412966.e schema:alternateName Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
    409 The-D Lab, Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
    410 schema:name Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
    411 Oncoradiomics SA, Clos Chanmurly 13, 4000, Liège, Belgium
    412 The-D Lab, Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
    413 rdf:type schema:Organization
    414 grid-institutes:grid.4861.b schema:alternateName Bioinformatics and Systems Biology Lab, University of Liège, Liège, Belgium
    415 GIGA-CRC in vivo Imaging, University of Liège, GIGA, Avenue de l’Hôpital 11, 4000, Liege, Belgium
    416 schema:name Bioinformatics and Systems Biology Lab, University of Liège, Liège, Belgium
    417 GIGA-CRC in vivo Imaging, University of Liège, GIGA, Avenue de l’Hôpital 11, 4000, Liege, Belgium
    418 rdf:type schema:Organization
    419 grid-institutes:grid.6289.5 schema:alternateName LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
    420 schema:name LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
    421 Radiation Oncology Department, University Hospital, Brest, France
    422 rdf:type schema:Organization
    423 grid-institutes:grid.63984.30 schema:alternateName Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada
    424 schema:name Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada
    425 rdf:type schema:Organization
     




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


    ...