Amnestic MCI future clinical status prediction using baseline MRI features

被引:23
作者
Duchesne, Simon [1 ,2 ]
Bocti, Christian [3 ,4 ]
De Sousa, Kathy [3 ]
Frisoni, Giovanni B. [5 ]
Chertkow, Howard [3 ,6 ,7 ,8 ]
Collins, D. Louis [9 ]
机构
[1] Ctr Rech Univ Laval Robert Giffard, Quebec City, PQ G1J 2G3, Canada
[2] Univ Laval, Dept Radiol, Quebec City, PQ, Canada
[3] McGill Univ, Sir Mortimer B Davis Jewish Gen Hosp, Lady Davis Inst, Bloomfield Ctr Res Aging, Montreal, PQ, Canada
[4] Univ Montreal, Dept Med, Maisonneuve Rosemont Hosp, Div Neurol, Montreal, PQ H3C 3J7, Canada
[5] IRCCS San Giovanni di Dio, Lab Epidemiol Neuroimaging & Telemed, Brescia, Italy
[6] McGill Univ, Dept Neurol & Neurosurg, Montreal, PQ, Canada
[7] McGill Univ, Div Geriatr Med, Montreal, PQ, Canada
[8] Inst Univ Geriatrie Montreal, Res Ctr, Montreal, PQ, Canada
[9] McGill Univ, Montreal Neurol Inst, Brain Imaging Ctr, Montreal, PQ, Canada
关键词
Mild cognitive impairment; Early detection; Magnetic resonance imaging; Automated computer classification; MILD COGNITIVE IMPAIRMENT; VOXEL-BASED MORPHOMETRY; PRODROMAL ALZHEIMERS-DISEASE; MEDIAL TEMPORAL-LOBE; GRAY-MATTER LOSS; HIPPOCAMPAL ATROPHY; BRAIN ATROPHY; PATTERN-CLASSIFICATION; ENTORHINAL CORTEX; AD;
D O I
10.1016/j.neurobiolaging.2008.09.003
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
Amnestic mild cognitive impairment (aMCI) individuals are known lobe at risk for progression to clinically probable Alzheimer's disease (AD) The objective of this work is to measure the accuracy of an automated classification technique based on clinical-quality, single time-point structural magnetic resonance imaging (MRI) scans for the retrospective prediction of future clinical status in aMCI Thirty-one aMCI research subjects were followed with annual clinical reassessment after baseline MRI. Twenty subjects progressed to probable AD within an average 22 (1 4) years [mean age 766 (4 7) years, MMSE 27 1 (2 3)]. while 11 remained non-demented on average 5 6 (2 6) years after baseline [mean age 73 3 (7 2) years. MMSE 28.2 (1 8)] Leave-one-out classification was performed within a multidimensional MRI feature space built from intensity and local volume estimate data of a reference group of 75 probable AD and 75 age-matched control subjects Prediction using aMCI data reached 81% accuracy, 70% sensitivity and 100% specificity This automated and objective method has potential in helping predict future clinical status in aMCI (C) 2008 Elsevier Inc All rights reserved
引用
收藏
页码:1606 / 1617
页数:12
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