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

VL - 11

SP - 435

EP - 449

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

ER -