Cited by 94Dementia and Cognitive Impairment ResearchMedical Image Segmentation TechniquesAlzheimer's disease research and treatments

Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease

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Chincarini, Andrea, Sensi, Francesco, Rei, Luca, Gemme, Gianluca, Squarcia, Sandro, Longo, Renata, Brun, Francesco, Tangaro, Sabina +8 more·2016·NeuroImage

Abstract

Background Structural MRI measures for monitoring Alzheimer's Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume.Methods We present a hippocampal segmentation algorithm and validate it on a gold-standard manual tracing database. We segmented 460 subjects from ADNI, each subject having been scanned twice at baseline, 12-month and 24month follow-up scan (1.5T, T1 MRI). We used the bilateral hippocampal volume v and its variation, measured as the annualized volume change Λ=δv/year(mm(3)/y). Four processing approaches with different complexity are compared to maximize the longitudinal information, and they are tested for cohort discrimination ability. Reference cohorts are Controls vs. Alzheimer's Disease (CTRL/AD) and CTRL vs. Mild Cognitive Impairment who subsequently progressed to AD dementia (CTRL/MCI-co). We discuss the conditions on v and the added value of Λ in discriminating subjects.Results The age-corrected bilateral annualized atrophy rate (%/year) were: -1.6 (0.6) for CTRL, -2.2 (1.0) for MCI-nc, -3.2 (1.2) for MCI-co and -4.0 (1.5) for AD. Combined (v, Λ) discrimination ability gave an Area under the ROC curve (auc)=0.93 for CTRL vs AD and auc=0.88 for CTRL vs MCI-co.Conclusions Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information.

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Crossref · 10.1016/j.neuroimage.2015.10.065

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Authors
Chincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., Bocchetta, M., Redolfi, A., Bosco, P., Boccardi, M., Frisoni, G. B., & Nobili, F.
Affiliations
  • Istituto Nazionale di Fisica Nucleare, Sezione di Genova (IT)
  • University of Trieste (IT)
  • Istituto Nazionale di Fisica Nucleare, Sezione di Bari (IT)
  • Centro San Giovanni di Dio Fatebenefratelli (IT)
  • University of Geneva (CH)
  • Ospedale Policlinico San Martino (IT)
Corresponding author
Chincarini, Andrea
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-01
Volume
125
Pages
834-847
ISSN
1053-8119 (ISSN-L)
OpenAlex
W1857124936
Cited by
94
References
60
Citations by year
2026: 3 · 2025: 4 · 2024: 8 · 2023: 10 · 2022: 3 · 2021: 12 · 2020: 11 · 2019: 7 · 2018: 12 · 2017: 16 · 2016: 7
FWCI
9.51
Type
journal article
Language
en
Open access
Locations
  • NeuroImage (acceptedVersion)
  • PubMed (publishedVersion)
  • Archive ouverte UNIGE (University of Geneva) (publishedVersion)
  • ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, other-oa)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
  • CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by)
  • Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
  • INFM-OAR (INFN Catania) (submittedVersion, cc-by)
  • Istituto Nazionale di Fisica Nucleare. Centro Nazionale Analisi Fotogrammi (cc-by)
Funders
  • Instituto Nazionale di Fisica Nucleare (D.R.N. 1229/20.12.2013)
  • Università degli Studi di Genova (7801/29.04.2011)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, hippocampal volume, longitudinal MRI, hippocampus segmentation, mild cognitive impairment, early detection, structural MRI, ADNI, hippocampal atrophy
MeSH terms
Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Female, Hippocampus, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Early Diagnosis

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