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CT-based volumetric measures obtained through deep learning : Association with biomarkers of neurodegeneration

Srikrishna, Meera ; Ashton, Nicholas J. ; Moscoso, Alexis ; Pereira, Joana B. LU ; Heckemann, Rolf A. ; van Westen, Danielle LU orcid ; Volpe, Giovanni ; Simrén, Joel ; Zettergren, Anna and Kern, Silke , et al. (2023) In Alzheimer's and Dementia
Abstract

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning–based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from... (More)

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning–based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)–based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)–based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.

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organization
publishing date
type
Contribution to journal
publication status
in press
subject
keywords
brain segmentation, cognition, CSF biomarkers, CT, deep learning, dementia, plasma biomarkers
in
Alzheimer's and Dementia
publisher
Wiley
external identifiers
  • pmid:37767905
  • scopus:85173102043
ISSN
1552-5260
DOI
10.1002/alz.13445
language
English
LU publication?
yes
id
2d635d45-1523-4cdf-abef-7a8e9fac6005
date added to LUP
2023-12-19 14:53:04
date last changed
2024-04-18 01:08:10
@article{2d635d45-1523-4cdf-abef-7a8e9fac6005,
  abstract     = {{<p>INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning–based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)–based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)–based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.</p>}},
  author       = {{Srikrishna, Meera and Ashton, Nicholas J. and Moscoso, Alexis and Pereira, Joana B. and Heckemann, Rolf A. and van Westen, Danielle and Volpe, Giovanni and Simrén, Joel and Zettergren, Anna and Kern, Silke and Wahlund, Lars Olof and Gyanwali, Bibek and Hilal, Saima and Ruifen, Joyce Chong and Zetterberg, Henrik and Blennow, Kaj and Westman, Eric and Chen, Christopher and Skoog, Ingmar and Schöll, Michael}},
  issn         = {{1552-5260}},
  keywords     = {{brain segmentation; cognition; CSF biomarkers; CT; deep learning; dementia; plasma biomarkers}},
  language     = {{eng}},
  publisher    = {{Wiley}},
  series       = {{Alzheimer's and Dementia}},
  title        = {{CT-based volumetric measures obtained through deep learning : Association with biomarkers of neurodegeneration}},
  url          = {{http://dx.doi.org/10.1002/alz.13445}},
  doi          = {{10.1002/alz.13445}},
  year         = {{2023}},
}