TY - JOUR
T1 - Differential diagnosis of neurodegenerative diseases using structural MRI data
AU - Koikkalainen, Juha
AU - Rhodius-Meester, Hanneke
AU - Tolonen, Antti
AU - Barkhof, Frederik
AU - Tijms, Betty M
AU - Lemstra, Afina W
AU - Tong, Tong
AU - Guerrero, Ricardo
AU - Schuh, Andreas
AU - Ledig, Christian
AU - Rueckert, Daniel
AU - Soininen, Hilkka
AU - Remes, Anne M
AU - Waldemar, Gunhild
AU - Hasselbalch, Steen
AU - Mecocci, Patrizia
AU - Van der Flier, Wiesje
AU - Lötjönen, Jyrki
PY - 2016
Y1 - 2016
N2 - 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 utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.
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 utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.
KW - Aged
KW - Brain Mapping
KW - Cerebral Infarction
KW - Diagnosis, Differential
KW - Female
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Magnetic Resonance Imaging
KW - Male
KW - Mental Status Schedule
KW - Middle Aged
KW - Neurodegenerative Diseases
KW - Retrospective Studies
KW - White Matter
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1016/j.nicl.2016.02.019
DO - 10.1016/j.nicl.2016.02.019
M3 - Journal article
C2 - 27104138
SN - 2213-1582
VL - 11
SP - 435
EP - 449
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
ER -