Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-07

AUTHORS

Sang Min Lee, Se Hyung Kim, Jeong Min Lee, Seock-Ah Im, Yung-Jue Bang, Woo Ho Kim, Min A Kim, Han-Kwang Yang, Hyuk-Joon Lee, Won Jun Kang, Joon Koo Han, Byung Ihn Choi

ABSTRACT

BACKGROUND: To investigate the utility of CT volumetry for primary gastric lesions in the prediction of pathologic response to neoadjuvant chemotherapy in patients with resectable advanced gastric cancer (AGC). MATERIALS AND METHODS: Thirty-three consecutive patients with resectable AGC stage >or=T2 and N1), who had been treated with neoadjuvant chemotherapy and radical gastric resection, were prospectively enrolled in this study. There were 30 men and 3 women with a mean age of 53.8 years. Contrast-enhanced CT was obtained after gastric distention with air before and after chemotherapy using a MDCT scanner. Pre- and post-chemotherapy thickness or short diameter and volume of the primary gastric tumor and largest lymph node (LN), were measured using a dedicated 3D software by two radiologists in consensus. PET/CT was also performed and the peak standardized uptake value (SUV) of primary gastric tumor and largest LN before and after chemotherapy was measured. The percentage diameter, volume, and SUV reduction rates for both the primary gastric tumor and the LN, were calculated and correlated with the histopathologic grades of regression using the Spearman correlation test. Differentiation between pathologic responders and nonresponders was assessed using receiver operating characteristic (ROC) analysis. RESULTS: Among the three CT parameters which showed significant correlation with the histopathologic grades of regression, the correlation factor was highest in the percentage volume reduction rate of primary gastric tumor (rho = 0.484, P = 0.004) followed by percentage volume reduction of the index node (rho = 0.397, P = 0.022), and percentage diameter reduction of the index node (rho = 0.359, P = 0.04). However, the percentage thickness decrease rate (P = 0.208) and the percentage SUV reduction rate (P = 0.619) of primary gastric tumor were not significantly correlated with the histopathologic grades of regression. When the optimal cutoff value of the percentage volume reduction rate of primary gastric tumor was determined to be 35.6%, a sensitivity of 100% (16/16) and a specificity of 58.8% (10/17) were achieved. CONCLUSION: CT volumetry for primary gastric tumor may be the most accurate tool in the prediction of pathologic response following neoadjuvant chemotherapy in patients with resectable AGC. More... »

PAGES

430

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00261-008-9420-8

DOI

http://dx.doi.org/10.1007/s00261-008-9420-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052473729

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18546037


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/2008/1112", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oncology and Carcinogenesis", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contrast Media", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gastrectomy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lymphatic Metastasis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neoadjuvant Therapy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neoplasm Staging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "ROC Curve", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Statistics, Nonparametric", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stomach Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Seoul National University", 
          "id": "https://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Sang Min", 
        "id": "sg:person.01315300754.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01315300754.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea", 
            "The Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Se Hyung", 
        "id": "sg:person.016434366660.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016434366660.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea", 
            "The Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Jeong Min", 
        "id": "sg:person.01266602714.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01266602714.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Im", 
        "givenName": "Seock-Ah", 
        "id": "sg:person.01212754274.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01212754274.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bang", 
        "givenName": "Yung-Jue", 
        "id": "sg:person.01363602512.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01363602512.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Pathology, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Woo Ho", 
        "id": "sg:person.016630576674.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016630576674.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Pathology, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Min A", 
        "id": "sg:person.0665463216.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665463216.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Surgery, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Han-Kwang", 
        "id": "sg:person.015267175234.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015267175234.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Surgery, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Hyuk-Joon", 
        "id": "sg:person.011242043254.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011242043254.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Yonsei University", 
          "id": "https://www.grid.ac/institutes/grid.15444.30", 
          "name": [
            "Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kang", 
        "givenName": "Won Jun", 
        "id": "sg:person.01077644577.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077644577.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea", 
            "The Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Han", 
        "givenName": "Joon Koo", 
        "id": "sg:person.0647723014.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647723014.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Seoul National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412484.f", 
          "name": [
            "Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea", 
            "The Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Choi", 
        "givenName": "Byung Ihn", 
        "id": "sg:person.0716036214.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716036214.08"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.3322/canjclin.57.1.43", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005586480"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1054/bjoc.2000.1205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007971885", 
          "https://doi.org/10.1054/bjoc.2000.1205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10637-005-3690-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010498577", 
          "https://doi.org/10.1007/s10637-005-3690-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10637-005-3690-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010498577", 
          "https://doi.org/10.1007/s10637-005-3690-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.bjc.6601843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016404471", 
          "https://doi.org/10.1038/sj.bjc.6601843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.bjc.6601843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016404471", 
          "https://doi.org/10.1038/sj.bjc.6601843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00259-002-1029-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022997427", 
          "https://doi.org/10.1007/s00259-002-1029-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003840050072", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028995544", 
          "https://doi.org/10.1007/s003840050072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/92.3.205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032301848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2422051557", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034795705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000234072.16209.ab", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042728636"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000234072.16209.ab", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042728636"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-004-0273-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044172351", 
          "https://doi.org/10.1007/s00261-004-0273-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00261-004-0273-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044172351", 
          "https://doi.org/10.1007/s00261-004-0273-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1053/ctrv.2000.0164", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045565044"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1098-2388(199909)17:2<132::aid-ssu8>3.0.co;2-e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046229639"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2391050043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047603198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-200605000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048004292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-200605000-00005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048004292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3322/canjclin.55.2.74", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048020321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1097-0142(19940601)73:11<2680::aid-cncr2820731105>3.0.co;2-c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049079293"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2373041380", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049349264"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa055531", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050751169"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2363041101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051112834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejca.2006.01.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053067745"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1200/jco.2003.06.574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064203707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.04.1812", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069297318"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083066354", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiographics.19.1.g99ja0761", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083381949"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-07", 
    "datePublishedReg": "2009-07-01", 
    "description": "BACKGROUND: To investigate the utility of CT volumetry for primary gastric lesions in the prediction of pathologic response to neoadjuvant chemotherapy in patients with resectable advanced gastric cancer (AGC).\nMATERIALS AND METHODS: Thirty-three consecutive patients with resectable AGC stage >or=T2 and N1), who had been treated with neoadjuvant chemotherapy and radical gastric resection, were prospectively enrolled in this study. There were 30 men and 3 women with a mean age of 53.8 years. Contrast-enhanced CT was obtained after gastric distention with air before and after chemotherapy using a MDCT scanner. Pre- and post-chemotherapy thickness or short diameter and volume of the primary gastric tumor and largest lymph node (LN), were measured using a dedicated 3D software by two radiologists in consensus. PET/CT was also performed and the peak standardized uptake value (SUV) of primary gastric tumor and largest LN before and after chemotherapy was measured. The percentage diameter, volume, and SUV reduction rates for both the primary gastric tumor and the LN, were calculated and correlated with the histopathologic grades of regression using the Spearman correlation test. Differentiation between pathologic responders and nonresponders was assessed using receiver operating characteristic (ROC) analysis.\nRESULTS: Among the three CT parameters which showed significant correlation with the histopathologic grades of regression, the correlation factor was highest in the percentage volume reduction rate of primary gastric tumor (rho = 0.484, P = 0.004) followed by percentage volume reduction of the index node (rho = 0.397, P = 0.022), and percentage diameter reduction of the index node (rho = 0.359, P = 0.04). However, the percentage thickness decrease rate (P = 0.208) and the percentage SUV reduction rate (P = 0.619) of primary gastric tumor were not significantly correlated with the histopathologic grades of regression. When the optimal cutoff value of the percentage volume reduction rate of primary gastric tumor was determined to be 35.6%, a sensitivity of 100% (16/16) and a specificity of 58.8% (10/17) were achieved.\nCONCLUSION: CT volumetry for primary gastric tumor may be the most accurate tool in the prediction of pathologic response following neoadjuvant chemotherapy in patients with resectable AGC.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00261-008-9420-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297457", 
        "issn": [
          "2366-004X", 
          "2366-0058"
        ], 
        "name": "Abdominal Radiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "34"
      }
    ], 
    "name": "Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer", 
    "pagination": "430", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f9f34e75e0390560486a5c176ce7030cfb9ccca59672d40ae6f9c0e7986a5bd2"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "18546037"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9303672"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00261-008-9420-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052473729"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00261-008-9420-8", 
      "https://app.dimensions.ai/details/publication/pub.1052473729"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:30", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000373_0000000373/records_13087_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00261-008-9420-8"
  }
]
 

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/s00261-008-9420-8'

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/s00261-008-9420-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00261-008-9420-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00261-008-9420-8'


 

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

305 TRIPLES      21 PREDICATES      71 URIs      39 LITERALS      27 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00261-008-9420-8 schema:about N08713d7d75bc4e9db162ff8a77023dfe
2 N1eeae667616f4a5cadd9b3aaf5b68043
3 N24a294b2abda427ba07dcfc9ff9a82e4
4 N28e290d545d74db38aee244e85f8fbd1
5 N29210973f61b4dbdabe0794f48a373e9
6 N388206d2e3f84c848365146ca09bc377
7 N5377ee349d0446318fe8a3b228df4be5
8 N560c600c698e4611bfb849bbc248adba
9 N5dd93ff198ea42a0b498cdad4190b82f
10 N7350eac188b843c5986ce43551787567
11 Na00daacd72a947119c73539e6a4c6e0c
12 Nb38a8fa5cfec4c948af8f826589793a6
13 Nc3b7274c0aec4658947757f8d3599c57
14 Nc9dcd8c66ef04479b1e30232428fcef5
15 Ncc39b63e876b4a25a42d7cc90f0baaaa
16 Nd3b196cb7c9143e9abe393198874ef52
17 Ndcc8636ed982425abdeab2a7ea755337
18 Ne5bf5bcb6334444ab244466e1ed3753e
19 anzsrc-for:11
20 anzsrc-for:1112
21 schema:author Nc0a5aec534024989a5fa0acfc34d4226
22 schema:citation sg:pub.10.1007/s00259-002-1029-5
23 sg:pub.10.1007/s00261-004-0273-5
24 sg:pub.10.1007/s003840050072
25 sg:pub.10.1007/s10637-005-3690-6
26 sg:pub.10.1038/sj.bjc.6601843
27 sg:pub.10.1054/bjoc.2000.1205
28 https://app.dimensions.ai/details/publication/pub.1083066354
29 https://doi.org/10.1002/(sici)1098-2388(199909)17:2<132::aid-ssu8>3.0.co;2-e
30 https://doi.org/10.1002/1097-0142(19940601)73:11<2680::aid-cncr2820731105>3.0.co;2-c
31 https://doi.org/10.1016/j.ejca.2006.01.026
32 https://doi.org/10.1053/ctrv.2000.0164
33 https://doi.org/10.1056/nejmoa055531
34 https://doi.org/10.1093/jnci/92.3.205
35 https://doi.org/10.1097/00004728-200605000-00005
36 https://doi.org/10.1097/01.rct.0000234072.16209.ab
37 https://doi.org/10.1148/radiographics.19.1.g99ja0761
38 https://doi.org/10.1148/radiol.2363041101
39 https://doi.org/10.1148/radiol.2373041380
40 https://doi.org/10.1148/radiol.2391050043
41 https://doi.org/10.1148/radiol.2422051557
42 https://doi.org/10.1200/jco.2003.06.574
43 https://doi.org/10.2214/ajr.04.1812
44 https://doi.org/10.3322/canjclin.55.2.74
45 https://doi.org/10.3322/canjclin.57.1.43
46 schema:datePublished 2009-07
47 schema:datePublishedReg 2009-07-01
48 schema:description BACKGROUND: To investigate the utility of CT volumetry for primary gastric lesions in the prediction of pathologic response to neoadjuvant chemotherapy in patients with resectable advanced gastric cancer (AGC). MATERIALS AND METHODS: Thirty-three consecutive patients with resectable AGC stage >or=T2 and N1), who had been treated with neoadjuvant chemotherapy and radical gastric resection, were prospectively enrolled in this study. There were 30 men and 3 women with a mean age of 53.8 years. Contrast-enhanced CT was obtained after gastric distention with air before and after chemotherapy using a MDCT scanner. Pre- and post-chemotherapy thickness or short diameter and volume of the primary gastric tumor and largest lymph node (LN), were measured using a dedicated 3D software by two radiologists in consensus. PET/CT was also performed and the peak standardized uptake value (SUV) of primary gastric tumor and largest LN before and after chemotherapy was measured. The percentage diameter, volume, and SUV reduction rates for both the primary gastric tumor and the LN, were calculated and correlated with the histopathologic grades of regression using the Spearman correlation test. Differentiation between pathologic responders and nonresponders was assessed using receiver operating characteristic (ROC) analysis. RESULTS: Among the three CT parameters which showed significant correlation with the histopathologic grades of regression, the correlation factor was highest in the percentage volume reduction rate of primary gastric tumor (rho = 0.484, P = 0.004) followed by percentage volume reduction of the index node (rho = 0.397, P = 0.022), and percentage diameter reduction of the index node (rho = 0.359, P = 0.04). However, the percentage thickness decrease rate (P = 0.208) and the percentage SUV reduction rate (P = 0.619) of primary gastric tumor were not significantly correlated with the histopathologic grades of regression. When the optimal cutoff value of the percentage volume reduction rate of primary gastric tumor was determined to be 35.6%, a sensitivity of 100% (16/16) and a specificity of 58.8% (10/17) were achieved. CONCLUSION: CT volumetry for primary gastric tumor may be the most accurate tool in the prediction of pathologic response following neoadjuvant chemotherapy in patients with resectable AGC.
49 schema:genre research_article
50 schema:inLanguage en
51 schema:isAccessibleForFree false
52 schema:isPartOf N438728b783a84e9da25b1943433ec09a
53 Ne796d94144274287a92dd98abf3926ca
54 sg:journal.1297457
55 schema:name Usefulness of CT volumetry for primary gastric lesions in predicting pathologic response to neoadjuvant chemotherapy in advanced gastric cancer
56 schema:pagination 430
57 schema:productId N0fad62c43ddb4f27999394f08d16d6c6
58 N38e52d447ee8476d956ac18eab14fd3c
59 N4d2c9300108745049abd3885604bc9de
60 Naf8f997e792d4a88a57ab829a8a982ee
61 Ndda6a9f45395484296f77fdbe47eb525
62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052473729
63 https://doi.org/10.1007/s00261-008-9420-8
64 schema:sdDatePublished 2019-04-11T14:30
65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
66 schema:sdPublisher N372482a1da01405582fc23d57ac685a0
67 schema:url http://link.springer.com/10.1007%2Fs00261-008-9420-8
68 sgo:license sg:explorer/license/
69 sgo:sdDataset articles
70 rdf:type schema:ScholarlyArticle
71 N08713d7d75bc4e9db162ff8a77023dfe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name Gastrectomy
73 rdf:type schema:DefinedTerm
74 N0fad62c43ddb4f27999394f08d16d6c6 schema:name readcube_id
75 schema:value f9f34e75e0390560486a5c176ce7030cfb9ccca59672d40ae6f9c0e7986a5bd2
76 rdf:type schema:PropertyValue
77 N1eeae667616f4a5cadd9b3aaf5b68043 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Male
79 rdf:type schema:DefinedTerm
80 N23e33a7b011c45d4897334ad8328b7c3 rdf:first sg:person.011242043254.34
81 rdf:rest N8d230dd016df4fceb375c04244d13156
82 N24a294b2abda427ba07dcfc9ff9a82e4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Radiographic Image Interpretation, Computer-Assisted
84 rdf:type schema:DefinedTerm
85 N28e290d545d74db38aee244e85f8fbd1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Neoplasm Staging
87 rdf:type schema:DefinedTerm
88 N29210973f61b4dbdabe0794f48a373e9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Humans
90 rdf:type schema:DefinedTerm
91 N372482a1da01405582fc23d57ac685a0 schema:name Springer Nature - SN SciGraph project
92 rdf:type schema:Organization
93 N388206d2e3f84c848365146ca09bc377 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Imaging, Three-Dimensional
95 rdf:type schema:DefinedTerm
96 N38e52d447ee8476d956ac18eab14fd3c schema:name nlm_unique_id
97 schema:value 9303672
98 rdf:type schema:PropertyValue
99 N438728b783a84e9da25b1943433ec09a schema:issueNumber 4
100 rdf:type schema:PublicationIssue
101 N4d2c9300108745049abd3885604bc9de schema:name doi
102 schema:value 10.1007/s00261-008-9420-8
103 rdf:type schema:PropertyValue
104 N5377ee349d0446318fe8a3b228df4be5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Female
106 rdf:type schema:DefinedTerm
107 N559099ac033b4b1aaf1469091738cd47 rdf:first sg:person.016434366660.80
108 rdf:rest Naa21c154b5314abea76ac5a1a46b53d3
109 N560c600c698e4611bfb849bbc248adba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Sensitivity and Specificity
111 rdf:type schema:DefinedTerm
112 N5dd93ff198ea42a0b498cdad4190b82f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Adult
114 rdf:type schema:DefinedTerm
115 N6178482a30d64cdf8358d9b7a0f0a214 rdf:first sg:person.0665463216.06
116 rdf:rest Nd14b2d29a7454b61afb7f3595aa080e3
117 N7350eac188b843c5986ce43551787567 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Neoadjuvant Therapy
119 rdf:type schema:DefinedTerm
120 N8030983ae35543a9993d557af5e9e048 rdf:first sg:person.0647723014.95
121 rdf:rest N8123bf2d6e044498aa8716f4518d2601
122 N8123bf2d6e044498aa8716f4518d2601 rdf:first sg:person.0716036214.08
123 rdf:rest rdf:nil
124 N8d230dd016df4fceb375c04244d13156 rdf:first sg:person.01077644577.56
125 rdf:rest N8030983ae35543a9993d557af5e9e048
126 Na00daacd72a947119c73539e6a4c6e0c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name ROC Curve
128 rdf:type schema:DefinedTerm
129 Naa21c154b5314abea76ac5a1a46b53d3 rdf:first sg:person.01266602714.81
130 rdf:rest Ne01b8234417f486e9dd8936baaebe9fc
131 Naf8f997e792d4a88a57ab829a8a982ee schema:name dimensions_id
132 schema:value pub.1052473729
133 rdf:type schema:PropertyValue
134 Nb11da3e084134e0cb98105fcb31019d7 rdf:first sg:person.016630576674.47
135 rdf:rest N6178482a30d64cdf8358d9b7a0f0a214
136 Nb38a8fa5cfec4c948af8f826589793a6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Statistics, Nonparametric
138 rdf:type schema:DefinedTerm
139 Nc0a5aec534024989a5fa0acfc34d4226 rdf:first sg:person.01315300754.59
140 rdf:rest N559099ac033b4b1aaf1469091738cd47
141 Nc3b7274c0aec4658947757f8d3599c57 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Contrast Media
143 rdf:type schema:DefinedTerm
144 Nc9dcd8c66ef04479b1e30232428fcef5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
145 schema:name Lymphatic Metastasis
146 rdf:type schema:DefinedTerm
147 Ncc39b63e876b4a25a42d7cc90f0baaaa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Middle Aged
149 rdf:type schema:DefinedTerm
150 Nd0b425b6efd44c038d831b0dd7d6bd73 rdf:first sg:person.01363602512.42
151 rdf:rest Nb11da3e084134e0cb98105fcb31019d7
152 Nd14b2d29a7454b61afb7f3595aa080e3 rdf:first sg:person.015267175234.94
153 rdf:rest N23e33a7b011c45d4897334ad8328b7c3
154 Nd3b196cb7c9143e9abe393198874ef52 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Stomach Neoplasms
156 rdf:type schema:DefinedTerm
157 Ndcc8636ed982425abdeab2a7ea755337 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Aged
159 rdf:type schema:DefinedTerm
160 Ndda6a9f45395484296f77fdbe47eb525 schema:name pubmed_id
161 schema:value 18546037
162 rdf:type schema:PropertyValue
163 Ne01b8234417f486e9dd8936baaebe9fc rdf:first sg:person.01212754274.50
164 rdf:rest Nd0b425b6efd44c038d831b0dd7d6bd73
165 Ne5bf5bcb6334444ab244466e1ed3753e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Tomography, X-Ray Computed
167 rdf:type schema:DefinedTerm
168 Ne796d94144274287a92dd98abf3926ca schema:volumeNumber 34
169 rdf:type schema:PublicationVolume
170 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
171 schema:name Medical and Health Sciences
172 rdf:type schema:DefinedTerm
173 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
174 schema:name Oncology and Carcinogenesis
175 rdf:type schema:DefinedTerm
176 sg:journal.1297457 schema:issn 2366-004X
177 2366-0058
178 schema:name Abdominal Radiology
179 rdf:type schema:Periodical
180 sg:person.01077644577.56 schema:affiliation https://www.grid.ac/institutes/grid.15444.30
181 schema:familyName Kang
182 schema:givenName Won Jun
183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077644577.56
184 rdf:type schema:Person
185 sg:person.011242043254.34 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
186 schema:familyName Lee
187 schema:givenName Hyuk-Joon
188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011242043254.34
189 rdf:type schema:Person
190 sg:person.01212754274.50 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
191 schema:familyName Im
192 schema:givenName Seock-Ah
193 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01212754274.50
194 rdf:type schema:Person
195 sg:person.01266602714.81 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
196 schema:familyName Lee
197 schema:givenName Jeong Min
198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01266602714.81
199 rdf:type schema:Person
200 sg:person.01315300754.59 schema:affiliation https://www.grid.ac/institutes/grid.31501.36
201 schema:familyName Lee
202 schema:givenName Sang Min
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01315300754.59
204 rdf:type schema:Person
205 sg:person.01363602512.42 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
206 schema:familyName Bang
207 schema:givenName Yung-Jue
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01363602512.42
209 rdf:type schema:Person
210 sg:person.015267175234.94 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
211 schema:familyName Yang
212 schema:givenName Han-Kwang
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015267175234.94
214 rdf:type schema:Person
215 sg:person.016434366660.80 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
216 schema:familyName Kim
217 schema:givenName Se Hyung
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016434366660.80
219 rdf:type schema:Person
220 sg:person.016630576674.47 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
221 schema:familyName Kim
222 schema:givenName Woo Ho
223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016630576674.47
224 rdf:type schema:Person
225 sg:person.0647723014.95 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
226 schema:familyName Han
227 schema:givenName Joon Koo
228 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647723014.95
229 rdf:type schema:Person
230 sg:person.0665463216.06 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
231 schema:familyName Kim
232 schema:givenName Min A
233 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665463216.06
234 rdf:type schema:Person
235 sg:person.0716036214.08 schema:affiliation https://www.grid.ac/institutes/grid.412484.f
236 schema:familyName Choi
237 schema:givenName Byung Ihn
238 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716036214.08
239 rdf:type schema:Person
240 sg:pub.10.1007/s00259-002-1029-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022997427
241 https://doi.org/10.1007/s00259-002-1029-5
242 rdf:type schema:CreativeWork
243 sg:pub.10.1007/s00261-004-0273-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044172351
244 https://doi.org/10.1007/s00261-004-0273-5
245 rdf:type schema:CreativeWork
246 sg:pub.10.1007/s003840050072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028995544
247 https://doi.org/10.1007/s003840050072
248 rdf:type schema:CreativeWork
249 sg:pub.10.1007/s10637-005-3690-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010498577
250 https://doi.org/10.1007/s10637-005-3690-6
251 rdf:type schema:CreativeWork
252 sg:pub.10.1038/sj.bjc.6601843 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016404471
253 https://doi.org/10.1038/sj.bjc.6601843
254 rdf:type schema:CreativeWork
255 sg:pub.10.1054/bjoc.2000.1205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007971885
256 https://doi.org/10.1054/bjoc.2000.1205
257 rdf:type schema:CreativeWork
258 https://app.dimensions.ai/details/publication/pub.1083066354 schema:CreativeWork
259 https://doi.org/10.1002/(sici)1098-2388(199909)17:2<132::aid-ssu8>3.0.co;2-e schema:sameAs https://app.dimensions.ai/details/publication/pub.1046229639
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1002/1097-0142(19940601)73:11<2680::aid-cncr2820731105>3.0.co;2-c schema:sameAs https://app.dimensions.ai/details/publication/pub.1049079293
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1016/j.ejca.2006.01.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053067745
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1053/ctrv.2000.0164 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045565044
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1056/nejmoa055531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050751169
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1093/jnci/92.3.205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032301848
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1097/00004728-200605000-00005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048004292
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1097/01.rct.0000234072.16209.ab schema:sameAs https://app.dimensions.ai/details/publication/pub.1042728636
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1148/radiographics.19.1.g99ja0761 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083381949
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1148/radiol.2363041101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051112834
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1148/radiol.2373041380 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049349264
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1148/radiol.2391050043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047603198
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1148/radiol.2422051557 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034795705
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1200/jco.2003.06.574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064203707
286 rdf:type schema:CreativeWork
287 https://doi.org/10.2214/ajr.04.1812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069297318
288 rdf:type schema:CreativeWork
289 https://doi.org/10.3322/canjclin.55.2.74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048020321
290 rdf:type schema:CreativeWork
291 https://doi.org/10.3322/canjclin.57.1.43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005586480
292 rdf:type schema:CreativeWork
293 https://www.grid.ac/institutes/grid.15444.30 schema:alternateName Yonsei University
294 schema:name Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea
295 rdf:type schema:Organization
296 https://www.grid.ac/institutes/grid.31501.36 schema:alternateName Seoul National University
297 schema:name Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea
298 rdf:type schema:Organization
299 https://www.grid.ac/institutes/grid.412484.f schema:alternateName Seoul National University Hospital
300 schema:name Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
301 Department of Pathology, Seoul National University Hospital, Seoul, Korea
302 Department of Radiology, Seoul National University College of Medicine, 28, Yeongon-dong, Jongno-gu, 110-744, Seoul, Korea
303 Department of Surgery, Seoul National University Hospital, Seoul, Korea
304 The Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea
305 rdf:type schema:Organization
 




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


...