EORTC PET response criteria are more influenced by reconstruction inconsistencies than PERCIST but both benefit from the EARL harmonization program View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-05-30

AUTHORS

Charline Lasnon, Elske Quak, Pierre-Yves Le Roux, Philippe Robin, Michael S. Hofman, David Bourhis, Jason Callahan, David S. Binns, Cédric Desmonts, Pierre-Yves Salaun, Rodney J. Hicks, Nicolas Aide

ABSTRACT

BackgroundThis study evaluates the consistency of PET evaluation response criteria in solid tumours (PERCIST) and European Organisation for Research and Treatment of Cancer (EORTC) classification across different reconstruction algorithms and whether aligning standardized uptake values (SUVs) to the European Association of Nuclear Medicine acquisition (EANM)/EARL standards provides more consistent response classification.Materials and methodsBaseline (PET1) and response assessment (PET2) scans in 61 patients with non-small cell lung cancer were acquired in protocols compliant with the EANM guidelines and were reconstructed with point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction for optimal tumour detection and with a standardized ordered subset expectation maximization (OSEM) reconstruction known to fulfil EANM harmonizing standards. Patients were recruited in three centres. Following reconstruction, EQ.PET, a proprietary software solution was applied to the PSF ± TOF data (PSF ± TOF.EQ) to harmonize SUVs to the EANM standards. The impact of differing reconstructions on PERCIST and EORTC classification was evaluated using standardized uptake values corrected for lean body mass (SUL).ResultsUsing OSEMPET1/OSEMPET2 (standard scenario), responders displayed a reduction of −57.5% ± 23.4 and −63.9% ± 22.4 for SULmax and SULpeak, respectively, while progressing tumours had an increase of +63.4% ± 26.5 and +60.7% ± 19.6 for SULmax and SULpeak respectively. The use of PSF ± TOF reconstruction impacted the classification of tumour response. For example, taking the OSEMPET1/PSF ± TOFPET2 scenario reduced the apparent reduction in SUL in responding tumours (−39.7% ± 31.3 and −55.5% ± 26.3 for SULmax and SULpeak, respectively) but increased the apparent increase in SUL in progressing tumours (+130.0% ± 50.7 and +91.1% ± 39.6 for SULmax and SULpeak, respectively).Consequently, variation in reconstruction methodology (PSF ± TOFPET1/OSEMPET2 or OSEM PET1/PSF ± TOFPET2) led, respectively, to 11/61 (18.0%) and 10/61 (16.4%) PERCIST classification discordances and to 17/61 (28.9%) and 19/61 (31.1%) EORTC classification discordances. An agreement was better for these scenarios with application of the propriety filter, with kappa values of 1.00 and 0.95 compared to 0.75 and 0.77 for PERCIST and kappa values of 0.93 and 0.95 compared to 0.61 and 0.55 for EORTC, respectively.ConclusionPERCIST classification is less sensitive to reconstruction algorithm-dependent variability than EORTC classification but harmonizing SULs within the EARL program is equally effective with either. More... »

PAGES

17

References to SciGraph publications

  • 2016-09-19. Metabolic tumor volume predicts overall survival and local control in patients with stage III non-small cell lung cancer treated in ACRIN 6668/RTOG 0235 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-08-18. SUVref: reducing reconstruction-dependent variation in PET SUV in EJNMMI RESEARCH
  • 2017-01-31. Digital PET compliance to EARL accreditation specifications in EJNMMI PHYSICS
  • 2016-05-28. Comparison of RECIST, EORTC criteria and PERCIST for evaluation of early response to chemotherapy in patients with non-small-cell lung cancer in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2014-02-22. Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-07-30. Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2016-06-03. TLG-S criteria are superior to both EORTC and PERCIST for predicting outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-10-19. Erratum to: Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2015-08-18. Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2017-06-16. EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-11-14. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-04-06. Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2016-11-03. Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction in ANNALS OF NUCLEAR MEDICINE
  • 2013-06-11. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-01-25. Methodological Aspects of Multicenter Studies with Quantitative PET in POSITRON EMISSION TOMOGRAPHY
  • 2014-12-02. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40658-017-0185-4

    DOI

    http://dx.doi.org/10.1186/s40658-017-0185-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1085715498

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28560574


    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"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France", 
              "id": "http://www.grid.ac/institutes/grid.412043.0", 
              "name": [
                "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
                "INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lasnon", 
            "givenName": "Charline", 
            "id": "sg:person.01151563267.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01151563267.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Quak", 
            "givenName": "Elske", 
            "id": "sg:person.01233564514.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233564514.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.411766.3", 
              "name": [
                "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Le Roux", 
            "givenName": "Pierre-Yves", 
            "id": "sg:person.01156373352.82", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01156373352.82"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.411766.3", 
              "name": [
                "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, 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": "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia", 
              "id": "http://www.grid.ac/institutes/grid.1055.1", 
              "name": [
                "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hofman", 
            "givenName": "Michael S.", 
            "id": "sg:person.01012460764.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012460764.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.411766.3", 
              "name": [
                "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bourhis", 
            "givenName": "David", 
            "id": "sg:person.0636567456.08", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636567456.08"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia", 
              "id": "http://www.grid.ac/institutes/grid.1055.1", 
              "name": [
                "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Callahan", 
            "givenName": "Jason", 
            "id": "sg:person.0616621550.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616621550.24"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia", 
              "id": "http://www.grid.ac/institutes/grid.1055.1", 
              "name": [
                "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Binns", 
            "givenName": "David S.", 
            "id": "sg:person.01213140513.69", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213140513.69"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department, University Hospital, Caen, France", 
              "id": "http://www.grid.ac/institutes/grid.411149.8", 
              "name": [
                "Nuclear Medicine Department, University Hospital, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Desmonts", 
            "givenName": "C\u00e9dric", 
            "id": "sg:person.0715336115.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715336115.22"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France", 
              "id": "http://www.grid.ac/institutes/grid.411766.3", 
              "name": [
                "Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Salaun", 
            "givenName": "Pierre-Yves", 
            "id": "sg:person.01021766527.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021766527.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "The Sir Peter MacCallum Department of Oncology, the University of Melbourne, Melbourne, Australia", 
              "id": "http://www.grid.ac/institutes/grid.1008.9", 
              "name": [
                "Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia", 
                "The Sir Peter MacCallum Department of Oncology, the University of Melbourne, Melbourne, Australia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hicks", 
            "givenName": "Rodney J.", 
            "id": "sg:person.01121233254.97", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01121233254.97"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department, Caen University Hospital, Avenue C\u00f4te de Nacre, 14000, Caen, France", 
              "id": "http://www.grid.ac/institutes/grid.411149.8", 
              "name": [
                "INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France", 
                "Nuclear Medicine Department, University Hospital, Caen, France", 
                "Normandy University, Caen, France", 
                "Nuclear Medicine Department, Caen University Hospital, Avenue C\u00f4te de Nacre, 14000, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Aide", 
            "givenName": "Nicolas", 
            "id": "sg:person.01152406451.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152406451.51"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00259-016-3433-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019531568", 
              "https://doi.org/10.1007/s00259-016-3433-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-014-2961-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008918527", 
              "https://doi.org/10.1007/s00259-014-2961-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2465-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016939976", 
              "https://doi.org/10.1007/s00259-013-2465-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3165-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024360349", 
              "https://doi.org/10.1007/s00259-015-3165-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12149-016-1135-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031913269", 
              "https://doi.org/10.1007/s12149-016-1135-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2191-219x-1-16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025107920", 
              "https://doi.org/10.1186/2191-219x-1-16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2391-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016046039", 
              "https://doi.org/10.1007/s00259-013-2391-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-016-3520-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010653216", 
              "https://doi.org/10.1007/s00259-016-3520-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40658-017-0176-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083400264", 
              "https://doi.org/10.1186/s40658-017-0176-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3128-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037717808", 
              "https://doi.org/10.1007/s00259-015-3128-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-017-3740-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086069747", 
              "https://doi.org/10.1007/s00259-017-3740-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-61779-062-1_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013531254", 
              "https://doi.org/10.1007/978-1-61779-062-1_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1297-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021250910", 
              "https://doi.org/10.1007/s00259-009-1297-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-015-3230-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015721216", 
              "https://doi.org/10.1007/s00259-015-3230-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-014-2689-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024627703", 
              "https://doi.org/10.1007/s00259-014-2689-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-016-3420-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008488954", 
              "https://doi.org/10.1007/s00259-016-3420-7"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-05-30", 
        "datePublishedReg": "2017-05-30", 
        "description": "BackgroundThis study evaluates the consistency of PET evaluation response criteria in solid tumours (PERCIST) and European Organisation for Research and Treatment of Cancer (EORTC) classification across different reconstruction algorithms and whether aligning standardized uptake values (SUVs) to the European Association of Nuclear Medicine acquisition (EANM)/EARL standards provides more consistent response classification.Materials and methodsBaseline (PET1) and response assessment (PET2) scans in 61 patients with non-small cell lung cancer were acquired in protocols compliant with the EANM guidelines and were reconstructed with point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction for optimal tumour detection and with a standardized ordered subset expectation maximization (OSEM) reconstruction known to fulfil EANM harmonizing standards. Patients were recruited in three centres. Following reconstruction, EQ.PET, a proprietary software solution was applied to the PSF \u00b1 TOF data (PSF \u00b1 TOF.EQ) to harmonize SUVs to the EANM standards. The impact of differing reconstructions on PERCIST and EORTC classification was evaluated using standardized uptake values corrected for lean body mass (SUL).ResultsUsing OSEMPET1/OSEMPET2 (standard scenario), responders displayed a reduction of \u221257.5%\u2009\u00b1\u200923.4 and \u221263.9%\u2009\u00b1\u200922.4 for SULmax and SULpeak, respectively, while progressing tumours had an increase of +63.4%\u2009\u00b1\u200926.5 and +60.7%\u2009\u00b1\u200919.6 for SULmax and SULpeak respectively. The use of PSF\u2009\u00b1\u2009TOF reconstruction impacted the classification of tumour response. For example, taking the OSEMPET1/PSF\u2009\u00b1\u2009TOFPET2 scenario reduced the apparent reduction in SUL in responding tumours (\u221239.7%\u2009\u00b1\u200931.3 and \u221255.5%\u2009\u00b1\u200926.3 for SULmax and SULpeak, respectively) but increased the apparent increase in SUL in progressing tumours (+130.0%\u2009\u00b1\u200950.7 and +91.1%\u2009\u00b1\u200939.6 for SULmax and SULpeak, respectively).Consequently, variation in reconstruction methodology (PSF\u2009\u00b1\u2009TOFPET1/OSEMPET2 or OSEM PET1/PSF\u2009\u00b1\u2009TOFPET2) led, respectively, to 11/61 (18.0%) and 10/61 (16.4%) PERCIST classification discordances and to 17/61 (28.9%) and 19/61 (31.1%) EORTC classification discordances. An agreement was better for these scenarios with application of the propriety filter, with kappa values of 1.00 and 0.95 compared to 0.75 and 0.77 for PERCIST and kappa values of 0.93 and 0.95 compared to 0.61 and 0.55 for EORTC, respectively.ConclusionPERCIST classification is less sensitive to reconstruction algorithm-dependent variability than EORTC classification but harmonizing SULs within the EARL program is equally effective with either.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s40658-017-0185-4", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1051885", 
            "issn": [
              "2197-7364"
            ], 
            "name": "EJNMMI Physics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "4"
          }
        ], 
        "keywords": [
          "standardized uptake value", 
          "EORTC classification", 
          "classification discordance", 
          "uptake value", 
          "non-small cell lung cancer", 
          "response criteria", 
          "optimal tumour detection", 
          "PET Response Criteria", 
          "cell lung cancer", 
          "lean body mass", 
          "kappa values", 
          "lung cancer", 
          "tumor response", 
          "BackgroundThis study", 
          "response assessment", 
          "solid tumors", 
          "European Organization", 
          "EANM guidelines", 
          "PERCIST", 
          "tumors", 
          "European Association", 
          "SULpeak", 
          "SULmax", 
          "patients", 
          "response classification", 
          "body mass", 
          "tumor detection", 
          "subsets expectation maximization (OSEM) reconstruction", 
          "apparent increase", 
          "discordance", 
          "apparent reduction", 
          "EORTC", 
          "cancer", 
          "responders", 
          "cancer classification", 
          "treatment", 
          "criteria", 
          "harmonization program", 
          "EANM", 
          "association", 
          "flight reconstruction", 
          "reconstruction", 
          "increase", 
          "TOF reconstruction", 
          "guidelines", 
          "reduction", 
          "PET", 
          "classification", 
          "program", 
          "response", 
          "assessment", 
          "standards", 
          "center", 
          "study", 
          "protocol", 
          "different reconstruction algorithms", 
          "expectation maximization reconstruction", 
          "values", 
          "use", 
          "mass", 
          "function", 
          "Sul", 
          "data", 
          "detection", 
          "time", 
          "impact", 
          "variability", 
          "consistency", 
          "nuclear medicine acquisition", 
          "acquisition", 
          "proprietary software solutions", 
          "research", 
          "inconsistencies", 
          "organization", 
          "materials", 
          "variation", 
          "point spread function", 
          "agreement", 
          "reconstruction methodology", 
          "eq", 
          "scenarios", 
          "methodology", 
          "applications", 
          "reconstruction algorithm", 
          "example", 
          "TOF data", 
          "solution", 
          "filter", 
          "software solutions", 
          "algorithm"
        ], 
        "name": "EORTC PET response criteria are more influenced by reconstruction inconsistencies than PERCIST but both benefit from the EARL harmonization program", 
        "pagination": "17", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1085715498"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s40658-017-0185-4"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "28560574"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s40658-017-0185-4", 
          "https://app.dimensions.ai/details/publication/pub.1085715498"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-08-04T17:06", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_736.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s40658-017-0185-4"
      }
    ]
     

    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/s40658-017-0185-4'

    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/s40658-017-0185-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40658-017-0185-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40658-017-0185-4'


     

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

    312 TRIPLES      21 PREDICATES      131 URIs      107 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s40658-017-0185-4 schema:about anzsrc-for:11
    2 anzsrc-for:1112
    3 schema:author N2f2324519d4a4650bd3d40e4b2eab429
    4 schema:citation sg:pub.10.1007/978-1-61779-062-1_18
    5 sg:pub.10.1007/s00259-009-1297-4
    6 sg:pub.10.1007/s00259-013-2391-1
    7 sg:pub.10.1007/s00259-013-2465-0
    8 sg:pub.10.1007/s00259-014-2689-7
    9 sg:pub.10.1007/s00259-014-2961-x
    10 sg:pub.10.1007/s00259-015-3128-0
    11 sg:pub.10.1007/s00259-015-3165-8
    12 sg:pub.10.1007/s00259-015-3230-3
    13 sg:pub.10.1007/s00259-016-3420-7
    14 sg:pub.10.1007/s00259-016-3433-2
    15 sg:pub.10.1007/s00259-016-3520-4
    16 sg:pub.10.1007/s00259-017-3740-2
    17 sg:pub.10.1007/s12149-016-1135-2
    18 sg:pub.10.1186/2191-219x-1-16
    19 sg:pub.10.1186/s40658-017-0176-5
    20 schema:datePublished 2017-05-30
    21 schema:datePublishedReg 2017-05-30
    22 schema:description BackgroundThis study evaluates the consistency of PET evaluation response criteria in solid tumours (PERCIST) and European Organisation for Research and Treatment of Cancer (EORTC) classification across different reconstruction algorithms and whether aligning standardized uptake values (SUVs) to the European Association of Nuclear Medicine acquisition (EANM)/EARL standards provides more consistent response classification.Materials and methodsBaseline (PET1) and response assessment (PET2) scans in 61 patients with non-small cell lung cancer were acquired in protocols compliant with the EANM guidelines and were reconstructed with point-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction for optimal tumour detection and with a standardized ordered subset expectation maximization (OSEM) reconstruction known to fulfil EANM harmonizing standards. Patients were recruited in three centres. Following reconstruction, EQ.PET, a proprietary software solution was applied to the PSF ± TOF data (PSF ± TOF.EQ) to harmonize SUVs to the EANM standards. The impact of differing reconstructions on PERCIST and EORTC classification was evaluated using standardized uptake values corrected for lean body mass (SUL).ResultsUsing OSEMPET1/OSEMPET2 (standard scenario), responders displayed a reduction of −57.5% ± 23.4 and −63.9% ± 22.4 for SULmax and SULpeak, respectively, while progressing tumours had an increase of +63.4% ± 26.5 and +60.7% ± 19.6 for SULmax and SULpeak respectively. The use of PSF ± TOF reconstruction impacted the classification of tumour response. For example, taking the OSEMPET1/PSF ± TOFPET2 scenario reduced the apparent reduction in SUL in responding tumours (−39.7% ± 31.3 and −55.5% ± 26.3 for SULmax and SULpeak, respectively) but increased the apparent increase in SUL in progressing tumours (+130.0% ± 50.7 and +91.1% ± 39.6 for SULmax and SULpeak, respectively).Consequently, variation in reconstruction methodology (PSF ± TOFPET1/OSEMPET2 or OSEM PET1/PSF ± TOFPET2) led, respectively, to 11/61 (18.0%) and 10/61 (16.4%) PERCIST classification discordances and to 17/61 (28.9%) and 19/61 (31.1%) EORTC classification discordances. An agreement was better for these scenarios with application of the propriety filter, with kappa values of 1.00 and 0.95 compared to 0.75 and 0.77 for PERCIST and kappa values of 0.93 and 0.95 compared to 0.61 and 0.55 for EORTC, respectively.ConclusionPERCIST classification is less sensitive to reconstruction algorithm-dependent variability than EORTC classification but harmonizing SULs within the EARL program is equally effective with either.
    23 schema:genre article
    24 schema:isAccessibleForFree true
    25 schema:isPartOf N0aaa0b0f04814b01891676a8a99db0ff
    26 N77b83ffad8c344708fa04d0302198527
    27 sg:journal.1051885
    28 schema:keywords BackgroundThis study
    29 EANM
    30 EANM guidelines
    31 EORTC
    32 EORTC classification
    33 European Association
    34 European Organization
    35 PERCIST
    36 PET
    37 PET Response Criteria
    38 SULmax
    39 SULpeak
    40 Sul
    41 TOF data
    42 TOF reconstruction
    43 acquisition
    44 agreement
    45 algorithm
    46 apparent increase
    47 apparent reduction
    48 applications
    49 assessment
    50 association
    51 body mass
    52 cancer
    53 cancer classification
    54 cell lung cancer
    55 center
    56 classification
    57 classification discordance
    58 consistency
    59 criteria
    60 data
    61 detection
    62 different reconstruction algorithms
    63 discordance
    64 eq
    65 example
    66 expectation maximization reconstruction
    67 filter
    68 flight reconstruction
    69 function
    70 guidelines
    71 harmonization program
    72 impact
    73 inconsistencies
    74 increase
    75 kappa values
    76 lean body mass
    77 lung cancer
    78 mass
    79 materials
    80 methodology
    81 non-small cell lung cancer
    82 nuclear medicine acquisition
    83 optimal tumour detection
    84 organization
    85 patients
    86 point spread function
    87 program
    88 proprietary software solutions
    89 protocol
    90 reconstruction
    91 reconstruction algorithm
    92 reconstruction methodology
    93 reduction
    94 research
    95 responders
    96 response
    97 response assessment
    98 response classification
    99 response criteria
    100 scenarios
    101 software solutions
    102 solid tumors
    103 solution
    104 standardized uptake value
    105 standards
    106 study
    107 subsets expectation maximization (OSEM) reconstruction
    108 time
    109 treatment
    110 tumor detection
    111 tumor response
    112 tumors
    113 uptake value
    114 use
    115 values
    116 variability
    117 variation
    118 schema:name EORTC PET response criteria are more influenced by reconstruction inconsistencies than PERCIST but both benefit from the EARL harmonization program
    119 schema:pagination 17
    120 schema:productId N787de430df2e4bc5b5a16867730d1a88
    121 Ne032f549f3a84386af8ac74ead460592
    122 Ne0d02abaec39422a9dd297281ee8388f
    123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085715498
    124 https://doi.org/10.1186/s40658-017-0185-4
    125 schema:sdDatePublished 2022-08-04T17:06
    126 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    127 schema:sdPublisher N917e22974fe64a6199f9381a59a97b0e
    128 schema:url https://doi.org/10.1186/s40658-017-0185-4
    129 sgo:license sg:explorer/license/
    130 sgo:sdDataset articles
    131 rdf:type schema:ScholarlyArticle
    132 N0aaa0b0f04814b01891676a8a99db0ff schema:volumeNumber 4
    133 rdf:type schema:PublicationVolume
    134 N146ab686e33b4407a9cf0e1b4714c6c2 rdf:first sg:person.0636567456.08
    135 rdf:rest N423e898d5774488fa6c7822db96a5238
    136 N23c489dbadf44687ba3043a9349a866e rdf:first sg:person.01307140060.86
    137 rdf:rest Ncb5dd0fa298d43e8960968277a1d7962
    138 N2f2324519d4a4650bd3d40e4b2eab429 rdf:first sg:person.01151563267.05
    139 rdf:rest N6d9e93ff0d42433d96f4facab3a8ae25
    140 N423e898d5774488fa6c7822db96a5238 rdf:first sg:person.0616621550.24
    141 rdf:rest N685c7cc1679445fba756e9cbe3004b75
    142 N685c7cc1679445fba756e9cbe3004b75 rdf:first sg:person.01213140513.69
    143 rdf:rest Na4628b44e9264480a32f1e495fce0f1c
    144 N6d9e93ff0d42433d96f4facab3a8ae25 rdf:first sg:person.01233564514.37
    145 rdf:rest Ndc854173f30940faacdeb167fafb5629
    146 N77b83ffad8c344708fa04d0302198527 schema:issueNumber 1
    147 rdf:type schema:PublicationIssue
    148 N787de430df2e4bc5b5a16867730d1a88 schema:name dimensions_id
    149 schema:value pub.1085715498
    150 rdf:type schema:PropertyValue
    151 N917e22974fe64a6199f9381a59a97b0e schema:name Springer Nature - SN SciGraph project
    152 rdf:type schema:Organization
    153 Na4628b44e9264480a32f1e495fce0f1c rdf:first sg:person.0715336115.22
    154 rdf:rest Ne4a6fb4c690d4ee0aaf85b1d0372fc08
    155 Nb83e3a1391f84def8171e748dc69a3b9 rdf:first sg:person.01121233254.97
    156 rdf:rest Nd153103ba09b411e942763ced622b408
    157 Ncb5dd0fa298d43e8960968277a1d7962 rdf:first sg:person.01012460764.55
    158 rdf:rest N146ab686e33b4407a9cf0e1b4714c6c2
    159 Nd153103ba09b411e942763ced622b408 rdf:first sg:person.01152406451.51
    160 rdf:rest rdf:nil
    161 Ndc854173f30940faacdeb167fafb5629 rdf:first sg:person.01156373352.82
    162 rdf:rest N23c489dbadf44687ba3043a9349a866e
    163 Ne032f549f3a84386af8ac74ead460592 schema:name doi
    164 schema:value 10.1186/s40658-017-0185-4
    165 rdf:type schema:PropertyValue
    166 Ne0d02abaec39422a9dd297281ee8388f schema:name pubmed_id
    167 schema:value 28560574
    168 rdf:type schema:PropertyValue
    169 Ne4a6fb4c690d4ee0aaf85b1d0372fc08 rdf:first sg:person.01021766527.49
    170 rdf:rest Nb83e3a1391f84def8171e748dc69a3b9
    171 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    172 schema:name Medical and Health Sciences
    173 rdf:type schema:DefinedTerm
    174 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    175 schema:name Oncology and Carcinogenesis
    176 rdf:type schema:DefinedTerm
    177 sg:journal.1051885 schema:issn 2197-7364
    178 schema:name EJNMMI Physics
    179 schema:publisher Springer Nature
    180 rdf:type schema:Periodical
    181 sg:person.01012460764.55 schema:affiliation grid-institutes:grid.1055.1
    182 schema:familyName Hofman
    183 schema:givenName Michael S.
    184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01012460764.55
    185 rdf:type schema:Person
    186 sg:person.01021766527.49 schema:affiliation grid-institutes:grid.411766.3
    187 schema:familyName Salaun
    188 schema:givenName Pierre-Yves
    189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01021766527.49
    190 rdf:type schema:Person
    191 sg:person.01121233254.97 schema:affiliation grid-institutes:grid.1008.9
    192 schema:familyName Hicks
    193 schema:givenName Rodney J.
    194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01121233254.97
    195 rdf:type schema:Person
    196 sg:person.01151563267.05 schema:affiliation grid-institutes:grid.412043.0
    197 schema:familyName Lasnon
    198 schema:givenName Charline
    199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01151563267.05
    200 rdf:type schema:Person
    201 sg:person.01152406451.51 schema:affiliation grid-institutes:grid.411149.8
    202 schema:familyName Aide
    203 schema:givenName Nicolas
    204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152406451.51
    205 rdf:type schema:Person
    206 sg:person.01156373352.82 schema:affiliation grid-institutes:grid.411766.3
    207 schema:familyName Le Roux
    208 schema:givenName Pierre-Yves
    209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01156373352.82
    210 rdf:type schema:Person
    211 sg:person.01213140513.69 schema:affiliation grid-institutes:grid.1055.1
    212 schema:familyName Binns
    213 schema:givenName David S.
    214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213140513.69
    215 rdf:type schema:Person
    216 sg:person.01233564514.37 schema:affiliation grid-institutes:None
    217 schema:familyName Quak
    218 schema:givenName Elske
    219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233564514.37
    220 rdf:type schema:Person
    221 sg:person.01307140060.86 schema:affiliation grid-institutes:grid.411766.3
    222 schema:familyName Robin
    223 schema:givenName Philippe
    224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01307140060.86
    225 rdf:type schema:Person
    226 sg:person.0616621550.24 schema:affiliation grid-institutes:grid.1055.1
    227 schema:familyName Callahan
    228 schema:givenName Jason
    229 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616621550.24
    230 rdf:type schema:Person
    231 sg:person.0636567456.08 schema:affiliation grid-institutes:grid.411766.3
    232 schema:familyName Bourhis
    233 schema:givenName David
    234 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636567456.08
    235 rdf:type schema:Person
    236 sg:person.0715336115.22 schema:affiliation grid-institutes:grid.411149.8
    237 schema:familyName Desmonts
    238 schema:givenName Cédric
    239 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715336115.22
    240 rdf:type schema:Person
    241 sg:pub.10.1007/978-1-61779-062-1_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013531254
    242 https://doi.org/10.1007/978-1-61779-062-1_18
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1007/s00259-009-1297-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021250910
    245 https://doi.org/10.1007/s00259-009-1297-4
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1007/s00259-013-2391-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016046039
    248 https://doi.org/10.1007/s00259-013-2391-1
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1007/s00259-013-2465-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016939976
    251 https://doi.org/10.1007/s00259-013-2465-0
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1007/s00259-014-2689-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024627703
    254 https://doi.org/10.1007/s00259-014-2689-7
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1007/s00259-014-2961-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1008918527
    257 https://doi.org/10.1007/s00259-014-2961-x
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1007/s00259-015-3128-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037717808
    260 https://doi.org/10.1007/s00259-015-3128-0
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1007/s00259-015-3165-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024360349
    263 https://doi.org/10.1007/s00259-015-3165-8
    264 rdf:type schema:CreativeWork
    265 sg:pub.10.1007/s00259-015-3230-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015721216
    266 https://doi.org/10.1007/s00259-015-3230-3
    267 rdf:type schema:CreativeWork
    268 sg:pub.10.1007/s00259-016-3420-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008488954
    269 https://doi.org/10.1007/s00259-016-3420-7
    270 rdf:type schema:CreativeWork
    271 sg:pub.10.1007/s00259-016-3433-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019531568
    272 https://doi.org/10.1007/s00259-016-3433-2
    273 rdf:type schema:CreativeWork
    274 sg:pub.10.1007/s00259-016-3520-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010653216
    275 https://doi.org/10.1007/s00259-016-3520-4
    276 rdf:type schema:CreativeWork
    277 sg:pub.10.1007/s00259-017-3740-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086069747
    278 https://doi.org/10.1007/s00259-017-3740-2
    279 rdf:type schema:CreativeWork
    280 sg:pub.10.1007/s12149-016-1135-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031913269
    281 https://doi.org/10.1007/s12149-016-1135-2
    282 rdf:type schema:CreativeWork
    283 sg:pub.10.1186/2191-219x-1-16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025107920
    284 https://doi.org/10.1186/2191-219x-1-16
    285 rdf:type schema:CreativeWork
    286 sg:pub.10.1186/s40658-017-0176-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083400264
    287 https://doi.org/10.1186/s40658-017-0176-5
    288 rdf:type schema:CreativeWork
    289 grid-institutes:None schema:alternateName Nuclear Medicine Department, François Baclesse Cancer Centre, Caen, France
    290 schema:name Nuclear Medicine Department, François Baclesse Cancer Centre, Caen, France
    291 rdf:type schema:Organization
    292 grid-institutes:grid.1008.9 schema:alternateName The Sir Peter MacCallum Department of Oncology, the University of Melbourne, Melbourne, Australia
    293 schema:name Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia
    294 The Sir Peter MacCallum Department of Oncology, the University of Melbourne, Melbourne, Australia
    295 rdf:type schema:Organization
    296 grid-institutes:grid.1055.1 schema:alternateName Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia
    297 schema:name Cancer Imaging, Peter Mac Callum Cancer Institute, Parkville, Australia
    298 rdf:type schema:Organization
    299 grid-institutes:grid.411149.8 schema:alternateName Nuclear Medicine Department, Caen University Hospital, Avenue Côte de Nacre, 14000, Caen, France
    300 Nuclear Medicine Department, University Hospital, Caen, France
    301 schema:name INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France
    302 Normandy University, Caen, France
    303 Nuclear Medicine Department, Caen University Hospital, Avenue Côte de Nacre, 14000, Caen, France
    304 Nuclear Medicine Department, University Hospital, Caen, France
    305 rdf:type schema:Organization
    306 grid-institutes:grid.411766.3 schema:alternateName Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France
    307 schema:name Nuclear Medicine Department and EA 3878 IFR 148, University Hospital, Brest, France
    308 rdf:type schema:Organization
    309 grid-institutes:grid.412043.0 schema:alternateName INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France
    310 schema:name INSERM U1086 ANTICIPE, BioTICLA, Caen University, Caen, France
    311 Nuclear Medicine Department, François Baclesse Cancer Centre, Caen, France
    312 rdf:type schema:Organization
     




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


    ...