Logistic discriminant parametric mapping: a novel method for the pixel-based differential diagnosis of Parkinson’s disease View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1999-10

AUTHORS

Paul D. Acton, P. David Mozley, Hank F. Kung

ABSTRACT

Positron emission tomography (PET) and single-photon emission tomography (SPET) imaging of the dopaminergic system is a powerful tool for distinguishing groups of patients with neurodegenerative disorders, such as Parkinson's disease (PD). However, the differential diagnosis of individual subjects presenting early in the progress of the disease is much more difficult, particularly using region-of-interest analysis where small localized differences between subjects are diluted. In this paper we present a novel pixel-based technique using logistic discriminant analysis to distinguish between a group of PD patients and age-matched healthy controls. Simulated images of an anthropomorphic head phantom were used to test the sensitivity of the technique to striatal lesions of known size. The methodology was applied to real clinical SPET images of binding of technetium-99m labelled TRODAT-1 to dopamine transporters in PD patients (n=42) and age-matched controls (n=23). The discriminant model was trained on a subset (n=17) of patients for whom the diagnosis was unequivocal. Logistic discriminant parametric maps were obtained for all subjects, showing the probability distribution of pixels classified as being consistent with PD. The probability maps were corrected for correlated multiple comparisons assuming an isotropic Gaussian point spread function. Simulated lesion sizes measured by logistic discriminant parametric mapping (LDPM) gave strong correlations with the known data (r(2)=0. 985, P<0.001). LDPM correctly classified all PD patients (sensitivity 100%) and only misclassified one control (specificity 95%). All patients who had equivocal clinical symptoms associated with early onset PD (n=4) were correctly assigned to the patient group. Statistical parametric mapping (SPM) had a sensitivity of only 24% on the same patient group. LDPM is a powerful pixel-based tool for the differential diagnosis of patients with PD and healthy controls. The diagnosis of disease even before clinical symptoms become apparent may be possible, and ultimately this technique could be most useful in differentiating between several neurodegenerative disorders, incorporating images of multiple neuroreceptor systems. More... »

PAGES

1413-1423

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s002590050473

DOI

http://dx.doi.org/10.1007/s002590050473

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040306372

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10552082


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "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": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carrier Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diagnosis, Differential", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Discriminant Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Dopamine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Dopamine Plasma Membrane Transport Proteins", 
        "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": "Image Processing, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Membrane Glycoproteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Membrane Transport Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nerve Tissue Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Organotechnetium Compounds", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Parkinson Disease", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phantoms, Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiopharmaceuticals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, Emission-Computed, Single-Photon", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tropanes", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Pennsylvania", 
          "id": "https://www.grid.ac/institutes/grid.25879.31", 
          "name": [
            "Department of Radiology, University of Pennsylvania, 3700 Market Street, Room 305, Philadelphia, PA 19104, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Acton", 
        "givenName": "Paul D.", 
        "id": "sg:person.01032052701.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01032052701.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pennsylvania", 
          "id": "https://www.grid.ac/institutes/grid.25879.31", 
          "name": [
            "Department of Radiology, University of Pennsylvania, 3700 Market Street, Room 305, Philadelphia, PA 19104, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mozley", 
        "givenName": "P. David", 
        "id": "sg:person.0601735146.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601735146.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pennsylvania", 
          "id": "https://www.grid.ac/institutes/grid.25879.31", 
          "name": [
            "Department of Radiology, University of Pennsylvania, 3700 Market Street, Room 305, Philadelphia, PA 19104, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kung", 
        "givenName": "Hank F.", 
        "id": "sg:person.0615735606.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615735606.55"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1001/archneur.1994.00540150027011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000791166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.59.6.597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002542965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/brain/120.12.2187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004650266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-012161340-2/50018-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008194136"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000007896", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011203472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.870130212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014406379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.870130212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014406379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00881814", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015615607", 
          "https://doi.org/10.1007/bf00881814"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590050167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016297293", 
          "https://doi.org/10.1007/s002590050167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.52.suppl.78", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017716357"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ana.410380407", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022216822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.57.6.672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024065067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01254479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026914665", 
          "https://doi.org/10.1007/bf01254479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01254479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026914665", 
          "https://doi.org/10.1007/bf01254479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.57.3.278", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027555348"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/archneur.1990.00530120034007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028164106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0001867800025970", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028586331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590050374", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030137198", 
          "https://doi.org/10.1007/s002590050374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm198804073181402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031182950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.57.9.1047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031279576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03164771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031922706", 
          "https://doi.org/10.1007/bf03164771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03164771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031922706", 
          "https://doi.org/10.1007/bf03164771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp.62.2.133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034125453"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590050191", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036095970", 
          "https://doi.org/10.1007/s002590050191"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.460010306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037237655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mds.870130311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037756220"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-7091-6641-3_9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039545169", 
          "https://doi.org/10.1007/978-3-7091-6641-3_9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.460020402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041201593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hbm.460020402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041201593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ana.410280412", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041319167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0404.1996.tb00015.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042952121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/syn.890210202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043474197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s004150050168", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046389648", 
          "https://doi.org/10.1007/s004150050168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590050420", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047388481", 
          "https://doi.org/10.1007/s002590050420"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/syn.890090107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049173379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-510x(92)90007-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050837322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-510x(92)90007-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050837322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-7091-6842-4_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051671417", 
          "https://doi.org/10.1007/978-3-7091-6842-4_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/jcbfm.1991.122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051811404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/jcbfm.1991.122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051811404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-7091-6842-4_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051850066", 
          "https://doi.org/10.1007/978-3-7091-6842-4_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1098-2396(199806)29:2<128::aid-syn4>3.0.co;2-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051947419"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1192/bjp.173.2.116", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064173536"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1999-10", 
    "datePublishedReg": "1999-10-01", 
    "description": "Positron emission tomography (PET) and single-photon emission tomography (SPET) imaging of the dopaminergic system is a powerful tool for distinguishing groups of patients with neurodegenerative disorders, such as Parkinson's disease (PD). However, the differential diagnosis of individual subjects presenting early in the progress of the disease is much more difficult, particularly using region-of-interest analysis where small localized differences between subjects are diluted. In this paper we present a novel pixel-based technique using logistic discriminant analysis to distinguish between a group of PD patients and age-matched healthy controls. Simulated images of an anthropomorphic head phantom were used to test the sensitivity of the technique to striatal lesions of known size. The methodology was applied to real clinical SPET images of binding of technetium-99m labelled TRODAT-1 to dopamine transporters in PD patients (n=42) and age-matched controls (n=23). The discriminant model was trained on a subset (n=17) of patients for whom the diagnosis was unequivocal. Logistic discriminant parametric maps were obtained for all subjects, showing the probability distribution of pixels classified as being consistent with PD. The probability maps were corrected for correlated multiple comparisons assuming an isotropic Gaussian point spread function. Simulated lesion sizes measured by logistic discriminant parametric mapping (LDPM) gave strong correlations with the known data (r(2)=0. 985, P<0.001). LDPM correctly classified all PD patients (sensitivity 100%) and only misclassified one control (specificity 95%). All patients who had equivocal clinical symptoms associated with early onset PD (n=4) were correctly assigned to the patient group. Statistical parametric mapping (SPM) had a sensitivity of only 24% on the same patient group. LDPM is a powerful pixel-based tool for the differential diagnosis of patients with PD and healthy controls. The diagnosis of disease even before clinical symptoms become apparent may be possible, and ultimately this technique could be most useful in differentiating between several neurodegenerative disorders, incorporating images of multiple neuroreceptor systems.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s002590050473", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297401", 
        "issn": [
          "1619-7070", 
          "1619-7089"
        ], 
        "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "26"
      }
    ], 
    "name": "Logistic discriminant parametric mapping: a novel method for the pixel-based differential diagnosis of Parkinson\u2019s disease", 
    "pagination": "1413-1423", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c00071b5c9cd9362f60d50e9318bc660659201fd6880b4ac80f4043354d211f5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "10552082"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7606882"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s002590050473"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040306372"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s002590050473", 
      "https://app.dimensions.ai/details/publication/pub.1040306372"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:37", 
    "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/v90/0000000001_0000000264/records_8687_00000514.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs002590050473"
  }
]
 

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/s002590050473'

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/s002590050473'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s002590050473'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s002590050473'


 

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

293 TRIPLES      21 PREDICATES      88 URIs      43 LITERALS      31 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s002590050473 schema:about N0a3b66201ba444a8bd04750cb6a8cd5a
2 N0eb16996f2084c37991038c30d9ced3a
3 N1e19e8ff13454b8d98555ded71a3055a
4 N1ecfdacf99104e9dbb79c588affa9215
5 N2c15a4ea1aed4eaf8ff4c608413b9596
6 N352d6199fd27420ea82ba90322b43348
7 N5008d5c937554730ac23165edf185bae
8 N606420e3fc4340639419d087007c32a4
9 N622401813ffe47039ee577c01fa6b21d
10 N77dc494e1f7f4fc4961e95f92f3c63de
11 N8fa74af7192941308e849edde2a1d0d0
12 N98e2a1ad8b7c442aa1a99dc34625abb4
13 N9e34fe015837459aaaa1afeb55807bb0
14 Nb365242a21e24701a246bef43eaf7ad3
15 Nb4c9fc2ce7a246de8959254f5ebf84fc
16 Nba806884075a4490add226bb7f703125
17 Nbffc28823101494789ce87099ebdce51
18 Nce013b13766f45339a6418e6d74c2c35
19 Nd7b1fe7fa3de4567b88ac3edbd143381
20 Nd7eb53443da54482902297a6ed5a5f20
21 Ne3f4f7cbeee14c85be72fea0da31c433
22 Nec3bd6cd242f414b83c5d3c2340591df
23 anzsrc-for:11
24 anzsrc-for:1109
25 schema:author Ne4069f9fcaf0437489d3efc961a09db2
26 schema:citation sg:pub.10.1007/978-3-7091-6641-3_9
27 sg:pub.10.1007/978-3-7091-6842-4_2
28 sg:pub.10.1007/978-3-7091-6842-4_4
29 sg:pub.10.1007/bf00881814
30 sg:pub.10.1007/bf01254479
31 sg:pub.10.1007/bf03164771
32 sg:pub.10.1007/s002590050167
33 sg:pub.10.1007/s002590050191
34 sg:pub.10.1007/s002590050374
35 sg:pub.10.1007/s002590050420
36 sg:pub.10.1007/s004150050168
37 https://doi.org/10.1001/archneur.1990.00530120034007
38 https://doi.org/10.1001/archneur.1994.00540150027011
39 https://doi.org/10.1002/(sici)1098-2396(199806)29:2<128::aid-syn4>3.0.co;2-9
40 https://doi.org/10.1002/ana.410280412
41 https://doi.org/10.1002/ana.410380407
42 https://doi.org/10.1002/hbm.460010306
43 https://doi.org/10.1002/hbm.460020402
44 https://doi.org/10.1002/mds.870130212
45 https://doi.org/10.1002/mds.870130311
46 https://doi.org/10.1002/syn.890090107
47 https://doi.org/10.1002/syn.890210202
48 https://doi.org/10.1016/0022-510x(92)90007-8
49 https://doi.org/10.1016/b978-012161340-2/50018-4
50 https://doi.org/10.1017/s0001867800025970
51 https://doi.org/10.1038/jcbfm.1991.122
52 https://doi.org/10.1056/nejm198804073181402
53 https://doi.org/10.1093/brain/120.12.2187
54 https://doi.org/10.1111/j.1600-0404.1996.tb00015.x
55 https://doi.org/10.1136/jnnp.52.suppl.78
56 https://doi.org/10.1136/jnnp.57.3.278
57 https://doi.org/10.1136/jnnp.57.6.672
58 https://doi.org/10.1136/jnnp.57.9.1047
59 https://doi.org/10.1136/jnnp.59.6.597
60 https://doi.org/10.1136/jnnp.62.2.133
61 https://doi.org/10.1159/000007896
62 https://doi.org/10.1192/bjp.173.2.116
63 schema:datePublished 1999-10
64 schema:datePublishedReg 1999-10-01
65 schema:description Positron emission tomography (PET) and single-photon emission tomography (SPET) imaging of the dopaminergic system is a powerful tool for distinguishing groups of patients with neurodegenerative disorders, such as Parkinson's disease (PD). However, the differential diagnosis of individual subjects presenting early in the progress of the disease is much more difficult, particularly using region-of-interest analysis where small localized differences between subjects are diluted. In this paper we present a novel pixel-based technique using logistic discriminant analysis to distinguish between a group of PD patients and age-matched healthy controls. Simulated images of an anthropomorphic head phantom were used to test the sensitivity of the technique to striatal lesions of known size. The methodology was applied to real clinical SPET images of binding of technetium-99m labelled TRODAT-1 to dopamine transporters in PD patients (n=42) and age-matched controls (n=23). The discriminant model was trained on a subset (n=17) of patients for whom the diagnosis was unequivocal. Logistic discriminant parametric maps were obtained for all subjects, showing the probability distribution of pixels classified as being consistent with PD. The probability maps were corrected for correlated multiple comparisons assuming an isotropic Gaussian point spread function. Simulated lesion sizes measured by logistic discriminant parametric mapping (LDPM) gave strong correlations with the known data (r(2)=0. 985, P<0.001). LDPM correctly classified all PD patients (sensitivity 100%) and only misclassified one control (specificity 95%). All patients who had equivocal clinical symptoms associated with early onset PD (n=4) were correctly assigned to the patient group. Statistical parametric mapping (SPM) had a sensitivity of only 24% on the same patient group. LDPM is a powerful pixel-based tool for the differential diagnosis of patients with PD and healthy controls. The diagnosis of disease even before clinical symptoms become apparent may be possible, and ultimately this technique could be most useful in differentiating between several neurodegenerative disorders, incorporating images of multiple neuroreceptor systems.
66 schema:genre research_article
67 schema:inLanguage en
68 schema:isAccessibleForFree false
69 schema:isPartOf N32be6a31550142bab83a88f85f636b1d
70 Nb2a69cc8be804ac594916ac859d24b9c
71 sg:journal.1297401
72 schema:name Logistic discriminant parametric mapping: a novel method for the pixel-based differential diagnosis of Parkinson’s disease
73 schema:pagination 1413-1423
74 schema:productId N18c247b643e74e9f87379b8c31df1a82
75 N406d5b8b69fd45f98a12ecc01b4c6969
76 N425433dce210406e9d4058ad5dcb7981
77 N6de1dd26f0024f5d8fe602633d57c620
78 Ncf7a3fd9273a4c4881b75f0632961f42
79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040306372
80 https://doi.org/10.1007/s002590050473
81 schema:sdDatePublished 2019-04-10T21:37
82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
83 schema:sdPublisher Nc08f80bb42e3475fa04eb7da58bcd81c
84 schema:url http://link.springer.com/10.1007%2Fs002590050473
85 sgo:license sg:explorer/license/
86 sgo:sdDataset articles
87 rdf:type schema:ScholarlyArticle
88 N0a3b66201ba444a8bd04750cb6a8cd5a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Discriminant Analysis
90 rdf:type schema:DefinedTerm
91 N0eb16996f2084c37991038c30d9ced3a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Nerve Tissue Proteins
93 rdf:type schema:DefinedTerm
94 N18c247b643e74e9f87379b8c31df1a82 schema:name nlm_unique_id
95 schema:value 7606882
96 rdf:type schema:PropertyValue
97 N1e19e8ff13454b8d98555ded71a3055a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Phantoms, Imaging
99 rdf:type schema:DefinedTerm
100 N1ecfdacf99104e9dbb79c588affa9215 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Humans
102 rdf:type schema:DefinedTerm
103 N2c15a4ea1aed4eaf8ff4c608413b9596 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Dopamine
105 rdf:type schema:DefinedTerm
106 N32be6a31550142bab83a88f85f636b1d schema:volumeNumber 26
107 rdf:type schema:PublicationVolume
108 N352d6199fd27420ea82ba90322b43348 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Aged
110 rdf:type schema:DefinedTerm
111 N406d5b8b69fd45f98a12ecc01b4c6969 schema:name readcube_id
112 schema:value c00071b5c9cd9362f60d50e9318bc660659201fd6880b4ac80f4043354d211f5
113 rdf:type schema:PropertyValue
114 N425433dce210406e9d4058ad5dcb7981 schema:name dimensions_id
115 schema:value pub.1040306372
116 rdf:type schema:PropertyValue
117 N5008d5c937554730ac23165edf185bae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Radiopharmaceuticals
119 rdf:type schema:DefinedTerm
120 N57a819e7cabd4c7baffb385e1e39af9f rdf:first sg:person.0615735606.55
121 rdf:rest rdf:nil
122 N606420e3fc4340639419d087007c32a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Membrane Transport Proteins
124 rdf:type schema:DefinedTerm
125 N622401813ffe47039ee577c01fa6b21d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Dopamine Plasma Membrane Transport Proteins
127 rdf:type schema:DefinedTerm
128 N6de1dd26f0024f5d8fe602633d57c620 schema:name doi
129 schema:value 10.1007/s002590050473
130 rdf:type schema:PropertyValue
131 N77dc494e1f7f4fc4961e95f92f3c63de schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Image Processing, Computer-Assisted
133 rdf:type schema:DefinedTerm
134 N8fa74af7192941308e849edde2a1d0d0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Brain
136 rdf:type schema:DefinedTerm
137 N98e2a1ad8b7c442aa1a99dc34625abb4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Carrier Proteins
139 rdf:type schema:DefinedTerm
140 N9e34fe015837459aaaa1afeb55807bb0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Male
142 rdf:type schema:DefinedTerm
143 N9f1396b0dda847de8a0c5243d810e6fc rdf:first sg:person.0601735146.81
144 rdf:rest N57a819e7cabd4c7baffb385e1e39af9f
145 Nb2a69cc8be804ac594916ac859d24b9c schema:issueNumber 11
146 rdf:type schema:PublicationIssue
147 Nb365242a21e24701a246bef43eaf7ad3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Membrane Glycoproteins
149 rdf:type schema:DefinedTerm
150 Nb4c9fc2ce7a246de8959254f5ebf84fc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Tropanes
152 rdf:type schema:DefinedTerm
153 Nba806884075a4490add226bb7f703125 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Female
155 rdf:type schema:DefinedTerm
156 Nbffc28823101494789ce87099ebdce51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Parkinson Disease
158 rdf:type schema:DefinedTerm
159 Nc08f80bb42e3475fa04eb7da58bcd81c schema:name Springer Nature - SN SciGraph project
160 rdf:type schema:Organization
161 Nce013b13766f45339a6418e6d74c2c35 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Organotechnetium Compounds
163 rdf:type schema:DefinedTerm
164 Ncf7a3fd9273a4c4881b75f0632961f42 schema:name pubmed_id
165 schema:value 10552082
166 rdf:type schema:PropertyValue
167 Nd7b1fe7fa3de4567b88ac3edbd143381 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Diagnosis, Differential
169 rdf:type schema:DefinedTerm
170 Nd7eb53443da54482902297a6ed5a5f20 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Sensitivity and Specificity
172 rdf:type schema:DefinedTerm
173 Ne3f4f7cbeee14c85be72fea0da31c433 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name Tomography, Emission-Computed, Single-Photon
175 rdf:type schema:DefinedTerm
176 Ne4069f9fcaf0437489d3efc961a09db2 rdf:first sg:person.01032052701.90
177 rdf:rest N9f1396b0dda847de8a0c5243d810e6fc
178 Nec3bd6cd242f414b83c5d3c2340591df schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Computer Simulation
180 rdf:type schema:DefinedTerm
181 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
182 schema:name Medical and Health Sciences
183 rdf:type schema:DefinedTerm
184 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
185 schema:name Neurosciences
186 rdf:type schema:DefinedTerm
187 sg:journal.1297401 schema:issn 1619-7070
188 1619-7089
189 schema:name European Journal of Nuclear Medicine and Molecular Imaging
190 rdf:type schema:Periodical
191 sg:person.01032052701.90 schema:affiliation https://www.grid.ac/institutes/grid.25879.31
192 schema:familyName Acton
193 schema:givenName Paul D.
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01032052701.90
195 rdf:type schema:Person
196 sg:person.0601735146.81 schema:affiliation https://www.grid.ac/institutes/grid.25879.31
197 schema:familyName Mozley
198 schema:givenName P. David
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601735146.81
200 rdf:type schema:Person
201 sg:person.0615735606.55 schema:affiliation https://www.grid.ac/institutes/grid.25879.31
202 schema:familyName Kung
203 schema:givenName Hank F.
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615735606.55
205 rdf:type schema:Person
206 sg:pub.10.1007/978-3-7091-6641-3_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039545169
207 https://doi.org/10.1007/978-3-7091-6641-3_9
208 rdf:type schema:CreativeWork
209 sg:pub.10.1007/978-3-7091-6842-4_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051671417
210 https://doi.org/10.1007/978-3-7091-6842-4_2
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/978-3-7091-6842-4_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051850066
213 https://doi.org/10.1007/978-3-7091-6842-4_4
214 rdf:type schema:CreativeWork
215 sg:pub.10.1007/bf00881814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015615607
216 https://doi.org/10.1007/bf00881814
217 rdf:type schema:CreativeWork
218 sg:pub.10.1007/bf01254479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026914665
219 https://doi.org/10.1007/bf01254479
220 rdf:type schema:CreativeWork
221 sg:pub.10.1007/bf03164771 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031922706
222 https://doi.org/10.1007/bf03164771
223 rdf:type schema:CreativeWork
224 sg:pub.10.1007/s002590050167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016297293
225 https://doi.org/10.1007/s002590050167
226 rdf:type schema:CreativeWork
227 sg:pub.10.1007/s002590050191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036095970
228 https://doi.org/10.1007/s002590050191
229 rdf:type schema:CreativeWork
230 sg:pub.10.1007/s002590050374 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030137198
231 https://doi.org/10.1007/s002590050374
232 rdf:type schema:CreativeWork
233 sg:pub.10.1007/s002590050420 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047388481
234 https://doi.org/10.1007/s002590050420
235 rdf:type schema:CreativeWork
236 sg:pub.10.1007/s004150050168 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046389648
237 https://doi.org/10.1007/s004150050168
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1001/archneur.1990.00530120034007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028164106
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1001/archneur.1994.00540150027011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000791166
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1002/(sici)1098-2396(199806)29:2<128::aid-syn4>3.0.co;2-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051947419
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1002/ana.410280412 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041319167
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1002/ana.410380407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022216822
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1002/hbm.460010306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037237655
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1002/hbm.460020402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041201593
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1002/mds.870130212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014406379
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1002/mds.870130311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037756220
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1002/syn.890090107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049173379
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1002/syn.890210202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043474197
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1016/0022-510x(92)90007-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050837322
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1016/b978-012161340-2/50018-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008194136
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1017/s0001867800025970 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028586331
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1038/jcbfm.1991.122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051811404
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1056/nejm198804073181402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031182950
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1093/brain/120.12.2187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004650266
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1111/j.1600-0404.1996.tb00015.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1042952121
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1136/jnnp.52.suppl.78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017716357
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1136/jnnp.57.3.278 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027555348
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1136/jnnp.57.6.672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024065067
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1136/jnnp.57.9.1047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031279576
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1136/jnnp.59.6.597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002542965
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1136/jnnp.62.2.133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034125453
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1159/000007896 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011203472
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1192/bjp.173.2.116 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064173536
290 rdf:type schema:CreativeWork
291 https://www.grid.ac/institutes/grid.25879.31 schema:alternateName University of Pennsylvania
292 schema:name Department of Radiology, University of Pennsylvania, 3700 Market Street, Room 305, Philadelphia, PA 19104, USA, US
293 rdf:type schema:Organization
 




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


...