Automatic classification of MR scans in Alzheimer's disease.
Automatic classification of MR scans in Alzheimer's disease.
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DOI：
10.1093/brain/awm319
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发表时间：
2008-03
期刊：
影响因子：
14.5
通讯作者：
Frackowiak, Richard S. J.
中科院分区：
文献类型：
--
作者：
Kloeppel, Stefan;Stonnington, Cynthia M.;Chu, Carlton;Draganski, Bogdan;Scahill, Rachael I.;Rohrer, Jonathan D.;Fox, Nick C.;Jack, Clifford R., Jr.;Ashburner, John;Frackowiak, Richard S. J.
为了在诊断上有用，结构MRI必须在个体扫描中可靠地区分阿尔茨海默病（AD）和正常衰老。统计学习理论的最新进展已经导致支持向量机应用于MRI以检测各种疾病状态。本研究的目的是评估支持向量机如何成功地分配个人诊断，并确定是否可以使用多个扫描仪和不同中心的数据集相结合，以获得有效的扫描分类。我们使用线性支持向量机分类T1加权MR扫描的灰质段病理证实的AD患者和认知正常的老年人从两个中心获得不同的扫描设备。由于轻度AD的临床诊断是困难的，我们还测试了支持向量机的能力，以区分控制扫描的患者没有验尸确认。最后，我们试图使用这些方法来区分扫描患者患有AD与额颞叶变性。高达96%的病理证实的AD患者使用全脑图像进行了正确分类。成功合并了来自不同中心的数据，实现了独立分析的可比结果。重要的是，来自一个中心的数据可用于训练支持向量机，以准确区分从另一个中心获得的具有不同受试者和不同扫描仪设备的AD和正常老化扫描。在89%的病例中，轻度、临床上可能的AD患者和年龄/性别匹配的对照被正确分离，这与最好的临床中心公布的诊断率一致。该方法正确地将89%的尸检确诊为AD或额颞叶变性的患者分配到各自的组中。我们的研究得出三个结论：第一，支持向量机成功地将AD患者从健康老年人中分离出来。其次，它们在两种不同形式的痴呆症的鉴别诊断中表现良好。第三，该方法是稳健的，可以推广到不同的中心。这表明了基于计算机的诊断图像分析在临床实践中的重要作用。
To be diagnostically useful, structural MRI must reliably distinguish Alzheimer’s disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.
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DOI：
10.1016/s0140-6736(20)32205-4
发表时间：
2021-04-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Scheltens P;De Strooper B;Kivipelto M;Holstege H;Chételat G;Teunissen CE;Cummings J;van der Flier WM
通讯作者：
van der Flier WM
影响因子：
5.7
作者：
Herholz, K;Salmon, E;Heiss, WD
通讯作者：
Heiss, WD
影响因子：
9.9
作者：
Jack, CR;Shiung, MM;Petersen, RC
通讯作者：
Petersen, RC
影响因子：
9.9
作者：
Gosche, KM;Mortimer, JA;Snowdon, DA
通讯作者：
Snowdon, DA
影响因子：
5.7
作者：
Kawasaki, Yasuhiro;Suzuki, Michio;Kurachi, Masayoshi
通讯作者：
Kurachi, Masayoshi