Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-02-15

AUTHORS

Yaou Liu, Di Dong, Liwen Zhang, Yali Zang, Yunyun Duan, Xiaolu Qiu, Jing Huang, Huiqing Dong, Frederik Barkhof, Chaoen Hu, Mengjie Fang, Jie Tian, Kuncheng Li

ABSTRACT

ObjectiveTo develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).MethodsWe retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts.ResultsNine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort).ConclusionsA validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD.Key Points• Radiomic features of spinal cord lesions in MS and NMOSD were different.• Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD. More... »

PAGES

4670-4677

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-019-06026-w

DOI

http://dx.doi.org/10.1007/s00330-019-06026-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112158356

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30770971


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Area Under Curve", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cohort Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diagnosis, Differential", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetic Resonance Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Multiple Sclerosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neuromyelitis Optica", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Spinal Cord", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.413259.8", 
          "name": [
            "Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, People\u2019s Republic of China", 
            "Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People\u2019s Republic of China", 
            "Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands", 
            "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Yaou", 
        "id": "sg:person.011415272704.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011415272704.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dong", 
        "givenName": "Di", 
        "id": "sg:person.01020125013.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020125013.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Liwen", 
        "id": "sg:person.015667204060.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015667204060.04"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zang", 
        "givenName": "Yali", 
        "id": "sg:person.013742113113.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013742113113.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.411617.4", 
          "name": [
            "Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, People\u2019s Republic of China", 
            "Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Duan", 
        "givenName": "Yunyun", 
        "id": "sg:person.01245052727.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245052727.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.413259.8", 
          "name": [
            "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Qiu", 
        "givenName": "Xiaolu", 
        "id": "sg:person.016224016203.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016224016203.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.413259.8", 
          "name": [
            "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huang", 
        "givenName": "Jing", 
        "id": "sg:person.0704010427.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704010427.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Neurology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.413259.8", 
          "name": [
            "Department of Neurology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dong", 
        "givenName": "Huiqing", 
        "id": "sg:person.01163570326.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163570326.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institutes of Neurology and Healthcare Engineering, UCL, London, UK", 
          "id": "http://www.grid.ac/institutes/grid.83440.3b", 
          "name": [
            "Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands", 
            "Institutes of Neurology and Healthcare Engineering, UCL, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Barkhof", 
        "givenName": "Frederik", 
        "id": "sg:person.07542735637.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07542735637.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Chaoen", 
        "id": "sg:person.015412033345.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015412033345.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fang", 
        "givenName": "Mengjie", 
        "id": "sg:person.015023131767.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015023131767.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chinese Academy of Sciences, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China", 
            "University of Chinese Academy of Sciences, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tian", 
        "givenName": "Jie", 
        "id": "sg:person.01124562550.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124562550.66"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.413259.8", 
          "name": [
            "Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, People\u2019s Republic of China", 
            "Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Kuncheng", 
        "id": "sg:person.01326527402.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326527402.59"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1742-2094-9-14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047091067", 
          "https://doi.org/10.1186/1742-2094-9-14"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms5006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009469125", 
          "https://doi.org/10.1038/ncomms5006"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02-15", 
    "datePublishedReg": "2019-02-15", 
    "description": "ObjectiveTo develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).MethodsWe retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts.ResultsNine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort).ConclusionsA validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD.Key Points\u2022 Radiomic features of spinal cord lesions in MS and NMOSD were different.\u2022 Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00330-019-06026-w", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8292222", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8341182", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8330032", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8336722", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8369834", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7001705", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8345540", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1289120", 
        "issn": [
          "0938-7994", 
          "1432-1084"
        ], 
        "name": "European Radiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "29"
      }
    ], 
    "keywords": [
      "neuromyelitis optica spectrum disorder", 
      "spinal cord lesions", 
      "optica spectrum disorder", 
      "multiple sclerosis", 
      "cord lesions", 
      "validation cohort", 
      "radiomic features", 
      "radiomics signature", 
      "lesion length", 
      "EDSS score", 
      "NMOSD patients", 
      "radiomics nomogram", 
      "primary cohort", 
      "patient sex", 
      "differential diagnosis", 
      "spectrum disorder", 
      "pathological alterations", 
      "clinical discrimination", 
      "lesions", 
      "cohort", 
      "lesion heterogeneity", 
      "MRI measurements", 
      "ROC curve", 
      "good calibration", 
      "sclerosis", 
      "nomogram", 
      "disorders", 
      "sex", 
      "radiomics", 
      "EDSS", 
      "patients", 
      "ObjectiveTo", 
      "MethodsWe", 
      "disease", 
      "diagnosis", 
      "scores", 
      "ConclusionsA", 
      "calibration plots", 
      "alterations", 
      "features", 
      "heterogeneity", 
      "length", 
      "prediction model", 
      "discrimination", 
      "model", 
      "curves", 
      "signatures", 
      "area", 
      "measurements", 
      "initial set", 
      "respect", 
      "plots", 
      "performance", 
      "set", 
      "calibration"
    ], 
    "name": "Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder", 
    "pagination": "4670-4677", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112158356"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00330-019-06026-w"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30770971"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00330-019-06026-w", 
      "https://app.dimensions.ai/details/publication/pub.1112158356"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_816.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00330-019-06026-w"
  }
]
 

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/s00330-019-06026-w'

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/s00330-019-06026-w'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-019-06026-w'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-019-06026-w'


 

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

295 TRIPLES      21 PREDICATES      96 URIs      86 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00330-019-06026-w schema:about N0315a45bc15b49909e50029a9a39e9c3
2 N061f93884a70498482f450061d27d18a
3 N0aeef0ce5ec3445eb237dfdeea2b7b3d
4 N196618264b9247da95ac18483367d027
5 N375d14402c4a4c28ae23ee7bac91efd0
6 N5733459bc8e14cfbb85cf03cca5e4daa
7 N583088f2418e4ab5bd73698d6706f420
8 N6abc8050bf744f3a9aea4c7823fccff4
9 N796ba5c7bb954663871c96eccd9762d1
10 N817c494dc9fb4fcc8e3dc960d546ba19
11 N81cb90cf7eba42dd944f37fba19e7d21
12 N94b70169f0e14766a125215213fadb2f
13 Nea38db8f38af4e5bae2db3fb38445d73
14 Nfcc12b2df6354485ab6d307aa27ec335
15 anzsrc-for:11
16 anzsrc-for:1109
17 schema:author Nd716e242d0dd400ba41fa602aee6cd0e
18 schema:citation sg:pub.10.1038/ncomms5006
19 sg:pub.10.1186/1742-2094-9-14
20 schema:datePublished 2019-02-15
21 schema:datePublishedReg 2019-02-15
22 schema:description ObjectiveTo develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).MethodsWe retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts.ResultsNine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort).ConclusionsA validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD.Key Points• Radiomic features of spinal cord lesions in MS and NMOSD were different.• Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.
23 schema:genre article
24 schema:isAccessibleForFree true
25 schema:isPartOf N345887af52f546e2b038ab4f7ba1c812
26 N43af0b29f90d42b0ab9733856648b3dd
27 sg:journal.1289120
28 schema:keywords ConclusionsA
29 EDSS
30 EDSS score
31 MRI measurements
32 MethodsWe
33 NMOSD patients
34 ObjectiveTo
35 ROC curve
36 alterations
37 area
38 calibration
39 calibration plots
40 clinical discrimination
41 cohort
42 cord lesions
43 curves
44 diagnosis
45 differential diagnosis
46 discrimination
47 disease
48 disorders
49 features
50 good calibration
51 heterogeneity
52 initial set
53 length
54 lesion heterogeneity
55 lesion length
56 lesions
57 measurements
58 model
59 multiple sclerosis
60 neuromyelitis optica spectrum disorder
61 nomogram
62 optica spectrum disorder
63 pathological alterations
64 patient sex
65 patients
66 performance
67 plots
68 prediction model
69 primary cohort
70 radiomic features
71 radiomics
72 radiomics nomogram
73 radiomics signature
74 respect
75 sclerosis
76 scores
77 set
78 sex
79 signatures
80 spectrum disorder
81 spinal cord lesions
82 validation cohort
83 schema:name Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder
84 schema:pagination 4670-4677
85 schema:productId N1dbd11b51856463f9fcb3f5b6b2b2dba
86 N52ca5cebdb5e436e9d49004e543d0aa7
87 Ndcbaa780d9264a9bb85e1f0f29a6a285
88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112158356
89 https://doi.org/10.1007/s00330-019-06026-w
90 schema:sdDatePublished 2022-11-24T21:05
91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
92 schema:sdPublisher Nbd5306784b0b42399043030d08d23ed3
93 schema:url https://doi.org/10.1007/s00330-019-06026-w
94 sgo:license sg:explorer/license/
95 sgo:sdDataset articles
96 rdf:type schema:ScholarlyArticle
97 N0315a45bc15b49909e50029a9a39e9c3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Cohort Studies
99 rdf:type schema:DefinedTerm
100 N061f93884a70498482f450061d27d18a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Reproducibility of Results
102 rdf:type schema:DefinedTerm
103 N0aeef0ce5ec3445eb237dfdeea2b7b3d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Magnetic Resonance Imaging
105 rdf:type schema:DefinedTerm
106 N17837326e9aa4f2b94dd098abc631401 rdf:first sg:person.013742113113.51
107 rdf:rest N70b4847fc0914f7299acc7164154680d
108 N196618264b9247da95ac18483367d027 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Humans
110 rdf:type schema:DefinedTerm
111 N1dbd11b51856463f9fcb3f5b6b2b2dba schema:name pubmed_id
112 schema:value 30770971
113 rdf:type schema:PropertyValue
114 N345887af52f546e2b038ab4f7ba1c812 schema:issueNumber 9
115 rdf:type schema:PublicationIssue
116 N375d14402c4a4c28ae23ee7bac91efd0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Area Under Curve
118 rdf:type schema:DefinedTerm
119 N43af0b29f90d42b0ab9733856648b3dd schema:volumeNumber 29
120 rdf:type schema:PublicationVolume
121 N52ca5cebdb5e436e9d49004e543d0aa7 schema:name doi
122 schema:value 10.1007/s00330-019-06026-w
123 rdf:type schema:PropertyValue
124 N5733459bc8e14cfbb85cf03cca5e4daa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Prospective Studies
126 rdf:type schema:DefinedTerm
127 N583088f2418e4ab5bd73698d6706f420 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Male
129 rdf:type schema:DefinedTerm
130 N6abc8050bf744f3a9aea4c7823fccff4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Neuromyelitis Optica
132 rdf:type schema:DefinedTerm
133 N70b4847fc0914f7299acc7164154680d rdf:first sg:person.01245052727.42
134 rdf:rest N7fbbc1ba610842f0b7140fbaea840f00
135 N796ba5c7bb954663871c96eccd9762d1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Retrospective Studies
137 rdf:type schema:DefinedTerm
138 N7fbbc1ba610842f0b7140fbaea840f00 rdf:first sg:person.016224016203.11
139 rdf:rest Nc3e6fe54702f4e32bdfd920c339dd38a
140 N817c494dc9fb4fcc8e3dc960d546ba19 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Multiple Sclerosis
142 rdf:type schema:DefinedTerm
143 N81cb90cf7eba42dd944f37fba19e7d21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Diagnosis, Differential
145 rdf:type schema:DefinedTerm
146 N85bc54dd3e97486189667a02b2fa3d19 rdf:first sg:person.015023131767.19
147 rdf:rest Nc0a13fc6f7734e7abf3b510750a2dab2
148 N9228171349b6431d9ca6ab38af46b660 rdf:first sg:person.015667204060.04
149 rdf:rest N17837326e9aa4f2b94dd098abc631401
150 N94b70169f0e14766a125215213fadb2f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Spinal Cord
152 rdf:type schema:DefinedTerm
153 Nafff9684b0b341d38a7ffc8efc623a24 rdf:first sg:person.01326527402.59
154 rdf:rest rdf:nil
155 Nb36cd5924aa146e889231eb06ac95afe rdf:first sg:person.01163570326.85
156 rdf:rest Ne3b10838fc3647c6963bbb80eb068a06
157 Nbd5306784b0b42399043030d08d23ed3 schema:name Springer Nature - SN SciGraph project
158 rdf:type schema:Organization
159 Nc0a13fc6f7734e7abf3b510750a2dab2 rdf:first sg:person.01124562550.66
160 rdf:rest Nafff9684b0b341d38a7ffc8efc623a24
161 Nc3e6fe54702f4e32bdfd920c339dd38a rdf:first sg:person.0704010427.58
162 rdf:rest Nb36cd5924aa146e889231eb06ac95afe
163 Nd716e242d0dd400ba41fa602aee6cd0e rdf:first sg:person.011415272704.77
164 rdf:rest Ndf082cdef7b144dbbf4fb92f701a9d36
165 Ndcbaa780d9264a9bb85e1f0f29a6a285 schema:name dimensions_id
166 schema:value pub.1112158356
167 rdf:type schema:PropertyValue
168 Ndf082cdef7b144dbbf4fb92f701a9d36 rdf:first sg:person.01020125013.03
169 rdf:rest N9228171349b6431d9ca6ab38af46b660
170 Ne3b10838fc3647c6963bbb80eb068a06 rdf:first sg:person.07542735637.03
171 rdf:rest Nf78886e14e514e1a819743a40c7f7062
172 Nea38db8f38af4e5bae2db3fb38445d73 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Female
174 rdf:type schema:DefinedTerm
175 Nf78886e14e514e1a819743a40c7f7062 rdf:first sg:person.015412033345.26
176 rdf:rest N85bc54dd3e97486189667a02b2fa3d19
177 Nfcc12b2df6354485ab6d307aa27ec335 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Adult
179 rdf:type schema:DefinedTerm
180 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
181 schema:name Medical and Health Sciences
182 rdf:type schema:DefinedTerm
183 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
184 schema:name Neurosciences
185 rdf:type schema:DefinedTerm
186 sg:grant.7001705 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
187 rdf:type schema:MonetaryGrant
188 sg:grant.8292222 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
189 rdf:type schema:MonetaryGrant
190 sg:grant.8330032 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
191 rdf:type schema:MonetaryGrant
192 sg:grant.8336722 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
193 rdf:type schema:MonetaryGrant
194 sg:grant.8341182 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
195 rdf:type schema:MonetaryGrant
196 sg:grant.8345540 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
197 rdf:type schema:MonetaryGrant
198 sg:grant.8369834 http://pending.schema.org/fundedItem sg:pub.10.1007/s00330-019-06026-w
199 rdf:type schema:MonetaryGrant
200 sg:journal.1289120 schema:issn 0938-7994
201 1432-1084
202 schema:name European Radiology
203 schema:publisher Springer Nature
204 rdf:type schema:Periodical
205 sg:person.01020125013.03 schema:affiliation grid-institutes:grid.410726.6
206 schema:familyName Dong
207 schema:givenName Di
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020125013.03
209 rdf:type schema:Person
210 sg:person.01124562550.66 schema:affiliation grid-institutes:grid.410726.6
211 schema:familyName Tian
212 schema:givenName Jie
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124562550.66
214 rdf:type schema:Person
215 sg:person.011415272704.77 schema:affiliation grid-institutes:grid.413259.8
216 schema:familyName Liu
217 schema:givenName Yaou
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011415272704.77
219 rdf:type schema:Person
220 sg:person.01163570326.85 schema:affiliation grid-institutes:grid.413259.8
221 schema:familyName Dong
222 schema:givenName Huiqing
223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163570326.85
224 rdf:type schema:Person
225 sg:person.01245052727.42 schema:affiliation grid-institutes:grid.411617.4
226 schema:familyName Duan
227 schema:givenName Yunyun
228 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245052727.42
229 rdf:type schema:Person
230 sg:person.01326527402.59 schema:affiliation grid-institutes:grid.413259.8
231 schema:familyName Li
232 schema:givenName Kuncheng
233 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326527402.59
234 rdf:type schema:Person
235 sg:person.013742113113.51 schema:affiliation grid-institutes:grid.410726.6
236 schema:familyName Zang
237 schema:givenName Yali
238 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013742113113.51
239 rdf:type schema:Person
240 sg:person.015023131767.19 schema:affiliation grid-institutes:grid.410726.6
241 schema:familyName Fang
242 schema:givenName Mengjie
243 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015023131767.19
244 rdf:type schema:Person
245 sg:person.015412033345.26 schema:affiliation grid-institutes:grid.410726.6
246 schema:familyName Hu
247 schema:givenName Chaoen
248 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015412033345.26
249 rdf:type schema:Person
250 sg:person.015667204060.04 schema:affiliation grid-institutes:grid.410726.6
251 schema:familyName Zhang
252 schema:givenName Liwen
253 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015667204060.04
254 rdf:type schema:Person
255 sg:person.016224016203.11 schema:affiliation grid-institutes:grid.413259.8
256 schema:familyName Qiu
257 schema:givenName Xiaolu
258 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016224016203.11
259 rdf:type schema:Person
260 sg:person.0704010427.58 schema:affiliation grid-institutes:grid.413259.8
261 schema:familyName Huang
262 schema:givenName Jing
263 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704010427.58
264 rdf:type schema:Person
265 sg:person.07542735637.03 schema:affiliation grid-institutes:grid.83440.3b
266 schema:familyName Barkhof
267 schema:givenName Frederik
268 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07542735637.03
269 rdf:type schema:Person
270 sg:pub.10.1038/ncomms5006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009469125
271 https://doi.org/10.1038/ncomms5006
272 rdf:type schema:CreativeWork
273 sg:pub.10.1186/1742-2094-9-14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047091067
274 https://doi.org/10.1186/1742-2094-9-14
275 rdf:type schema:CreativeWork
276 grid-institutes:grid.410726.6 schema:alternateName University of Chinese Academy of Sciences, Beijing, China
277 schema:name CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
278 University of Chinese Academy of Sciences, Beijing, China
279 rdf:type schema:Organization
280 grid-institutes:grid.411617.4 schema:alternateName Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People’s Republic of China
281 schema:name Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, People’s Republic of China
282 Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People’s Republic of China
283 rdf:type schema:Organization
284 grid-institutes:grid.413259.8 schema:alternateName Department of Neurology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People’s Republic of China
285 Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People’s Republic of China
286 schema:name Department of Neurology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People’s Republic of China
287 Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands
288 Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, People’s Republic of China
289 Department of Radiology, Xuanwu Hospital, Capital Medical University, 100053, Beijing, People’s Republic of China
290 Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, 100050, Beijing, People’s Republic of China
291 rdf:type schema:Organization
292 grid-institutes:grid.83440.3b schema:alternateName Institutes of Neurology and Healthcare Engineering, UCL, London, UK
293 schema:name Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands
294 Institutes of Neurology and Healthcare Engineering, UCL, London, UK
295 rdf:type schema:Organization
 




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


...