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Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment

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Title: Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment
Authors: Goldman, Jean-Philippe, Jane Soler, Pablo, Zaghir, Jamil, Andrade Teixeira, Eliluane Perazio, Peretti, Debora, Garibotto, Valentina, Lovis, Christian
Source: ISSN: 0926-9630 ; Studies in health technology and informatics, vol. 316 (2024) p. 560-564.
Publication Year: 2024
Collection: Université de Genève: Archive ouverte UNIGE
Subject Terms: info:eu-repo/classification/ddc/616.0757, Nuclear Medicine, Brain molecular imaging reports, Text classification, Humans, Positron-Emission Tomography, Cognitive Dysfunction / diagnostic imaging, Cognitive Dysfunction / classification, Brain / diagnostic imaging, Machine Learning, Alzheimer Disease / diagnostic imaging, Alzheimer Disease / classification, Natural Language Processing, Support Vector Machine, Sensitivity and Specificity, Switzerland, Reproducibility of Results
Description: The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.
Document Type: article in journal/newspaper
Language: English
Relation: info:eu-repo/semantics/altIdentifier/pmid/39176804; unige:180154
Availability: https://archive-ouverte.unige.ch/unige:180154
Rights: info:eu-repo/semantics/openAccess
Accession Number: edsbas.81883433
Database: BASE
FullText Text:
  Availability: 0
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  – Url: https://archive-ouverte.unige.ch/unige:180154#
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  Data: Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment
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  Data: <searchLink fieldCode="AR" term="%22Goldman%2C+Jean-Philippe%22">Goldman, Jean-Philippe</searchLink><br /><searchLink fieldCode="AR" term="%22Jane+Soler%2C+Pablo%22">Jane Soler, Pablo</searchLink><br /><searchLink fieldCode="AR" term="%22Zaghir%2C+Jamil%22">Zaghir, Jamil</searchLink><br /><searchLink fieldCode="AR" term="%22Andrade+Teixeira%2C+Eliluane+Perazio%22">Andrade Teixeira, Eliluane Perazio</searchLink><br /><searchLink fieldCode="AR" term="%22Peretti%2C+Debora%22">Peretti, Debora</searchLink><br /><searchLink fieldCode="AR" term="%22Garibotto%2C+Valentina%22">Garibotto, Valentina</searchLink><br /><searchLink fieldCode="AR" term="%22Lovis%2C+Christian%22">Lovis, Christian</searchLink>
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  Data: <i>ISSN: 0926-9630 ; Studies in health technology and informatics, vol. 316 (2024) p. 560-564</i>.
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  Label: Publication Year
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  Data: 2024
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  Data: Université de Genève: Archive ouverte UNIGE
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  Data: <searchLink fieldCode="DE" term="%22info%3Aeu-repo%2Fclassification%2Fddc%2F616%2E0757%22">info:eu-repo/classification/ddc/616.0757</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+Medicine%22">Nuclear Medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+molecular+imaging+reports%22">Brain molecular imaging reports</searchLink><br /><searchLink fieldCode="DE" term="%22Text+classification%22">Text classification</searchLink><br /><searchLink fieldCode="DE" term="%22Humans%22">Humans</searchLink><br /><searchLink fieldCode="DE" term="%22Positron-Emission+Tomography%22">Positron-Emission Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Dysfunction+%2F+diagnostic+imaging%22">Cognitive Dysfunction / diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Dysfunction+%2F+classification%22">Cognitive Dysfunction / classification</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+%2F+diagnostic+imaging%22">Brain / diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning%22">Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer+Disease+%2F+diagnostic+imaging%22">Alzheimer Disease / diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer+Disease+%2F+classification%22">Alzheimer Disease / classification</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Support+Vector+Machine%22">Support Vector Machine</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+and+Specificity%22">Sensitivity and Specificity</searchLink><br /><searchLink fieldCode="DE" term="%22Switzerland%22">Switzerland</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducibility+of+Results%22">Reproducibility of Results</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.
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  Data: info:eu-repo/semantics/altIdentifier/pmid/39176804; unige:180154
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  Data: https://archive-ouverte.unige.ch/unige:180154
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  Data: info:eu-repo/semantics/openAccess
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  Data: edsbas.81883433
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RecordInfo BibRecord:
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    Languages:
      – Text: English
    Subjects:
      – SubjectFull: info:eu-repo/classification/ddc/616.0757
        Type: general
      – SubjectFull: Nuclear Medicine
        Type: general
      – SubjectFull: Brain molecular imaging reports
        Type: general
      – SubjectFull: Text classification
        Type: general
      – SubjectFull: Humans
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      – SubjectFull: Positron-Emission Tomography
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      – SubjectFull: Cognitive Dysfunction / diagnostic imaging
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      – SubjectFull: Cognitive Dysfunction / classification
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      – SubjectFull: Brain / diagnostic imaging
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      – SubjectFull: Machine Learning
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      – SubjectFull: Alzheimer Disease / diagnostic imaging
        Type: general
      – SubjectFull: Alzheimer Disease / classification
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      – SubjectFull: Natural Language Processing
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      – SubjectFull: Support Vector Machine
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      – SubjectFull: Sensitivity and Specificity
        Type: general
      – SubjectFull: Switzerland
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      – SubjectFull: Reproducibility of Results
        Type: general
    Titles:
      – TitleFull: Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment
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            NameFull: Goldman, Jean-Philippe
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              M: 01
              Type: published
              Y: 2024
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