Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Koikkalainen:2016:10.1016/j.nicl.2016.02.019,
author = {Koikkalainen, J and Rhodius-Meester, H and Tolonen, A and Barkhof, F and Tijms, B and Lemstra, AW and Tong, T and Guerrero, R and Schuh, A and Ledig, C and Rueckert, D and Soininen, H and Remes, AM and Waldemar, G and Hasselbalch, S and Mecocci, P and van, der Flier W and Lötjönen, J},
doi = {10.1016/j.nicl.2016.02.019},
journal = {NeuroImage: Clinical},
pages = {435--449},
title = {Differential diagnosis of neurodegenerative diseases using structural MRI data},
url = {http://dx.doi.org/10.1016/j.nicl.2016.02.019},
volume = {11},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia.Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making.A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by util
AU - Koikkalainen,J
AU - Rhodius-Meester,H
AU - Tolonen,A
AU - Barkhof,F
AU - Tijms,B
AU - Lemstra,AW
AU - Tong,T
AU - Guerrero,R
AU - Schuh,A
AU - Ledig,C
AU - Rueckert,D
AU - Soininen,H
AU - Remes,AM
AU - Waldemar,G
AU - Hasselbalch,S
AU - Mecocci,P
AU - van,der Flier W
AU - Lötjönen,J
DO - 10.1016/j.nicl.2016.02.019
EP - 449
PY - 2016///
SN - 2213-1582
SP - 435
TI - Differential diagnosis of neurodegenerative diseases using structural MRI data
T2 - NeuroImage: Clinical
UR - http://dx.doi.org/10.1016/j.nicl.2016.02.019
UR - http://hdl.handle.net/10044/1/30753
VL - 11
ER -