Jing Li1, Emanuele Antonecchia1,2, Marco Camerlenghi3, Agostino Chiaravalloti4,5, Qian Chu6,7, Alfonso Di Costanzo8, Zhen Li6,9, Lin Wan10, Xiangsong Zhang11, Nicola D'Ascenzo12,13, Orazio Schillaci2,14, Qingguo Xie15,16. 1. Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. 2. Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy. 3. NIM Competence Center for Digital Healthcare GmbH, Potsdamerplatz, 10, 10785, Berlin, Germany. 4. Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy. agostino.chiaravalloti@uniroma2.it. 5. Department of Biomedicine and Prevention, University of Tor Vergata, 86100, Rome, Italy. agostino.chiaravalloti@uniroma2.it. 6. Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road, Wuhan, 430030, China. 7. Department of Oncology, Tongji Hospital, Jiefang Avenue, Wuhan, 430030, China. 8. Universita degli Studi del Molise, Via Francesco de Sanctis, 1, 10115, Campobasso, Italy. 9. Department of Radiology, Tongji Hospital, Jiefang Avenue, Wuhan, 430030, China. 10. Department of Software Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. 11. The First Affiliated Hospital, Sun Yat-sen University, Zhongshan 2nd Road, Guangzhou, 510080, China. 12. Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. ndasc@hust.edu.cn. 13. Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy. ndasc@hust.edu.cn. 14. Department of Biomedicine and Prevention, University of Tor Vergata, 86100, Rome, Italy. 15. Department of Biomedical Engineering, Huazhong University of Science and Technology, Luoyu Road, Wuhan, 430074, China. qgxie@hust.edu.cn. 16. Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo NEUROMED I.R.C.C.S, Via Dell'Elettronica, 83008, Pozzilli, Italy. qgxie@hust.edu.cn.
Abstract
BACKGROUND: When Alzheimer's disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text]F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). We conducted [[Formula: see text]F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text]) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student's t-test. RESULTS: We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text]F]florbetaben uptake than LOAD patients in those four brain regions. CONCLUSIONS: Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.
BACKGROUND: When Alzheimer's disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [[Formula: see text]F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). We conducted [[Formula: see text]F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-[Formula: see text] ([Formula: see text]) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student's t-test. RESULTS: We found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [[Formula: see text]F]florbetaben uptake than LOAD patients in those four brain regions. CONCLUSIONS: Early-onset AD implies a very irregular pattern of [Formula: see text] deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.
Authors: Martin N Rossor; Nick C Fox; Catherine J Mummery; Jonathan M Schott; Jason D Warren Journal: Lancet Neurol Date: 2010-08 Impact factor: 44.182