Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.
Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.
复制标题
将基于 MR 的自动诊断方法应用于记忆诊所：一项前瞻性研究。
DOI：
10.3233/jad-150334
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Klöppel S;Peter J;Ludl A;Pilatus A;Maier S;Mader I;Heimbach B;Frings L;Egger K;Dukart J;Schroeter ML;Perneczky R;Häussermann P;Vach W;Urbach H;Teipel S;Hüll M;Abdulkadir A;Alzheimer’s Disease Neuroimaging Initiative
几项研究表明，应用于结构磁共振成像（MRI）的全自动模式识别方法有助于痴呆症的诊断，但这些结论是基于高度预选的样本，与痴呆症诊所的样本有很大不同。在单个痴呆诊所，我们评估了线性支持向量机训练的完全不相关的数据区分阿尔茨海默病（AD），额颞叶痴呆（FTD），路易体痴呆和基于3D-T1加权MRI数据集的健康衰老的能力。此外，我们预测轻度认知障碍（MCI）受试者在基线时进展为AD，并自动量化flair图像中的白质高强度。将额外招募的健康老年人与痴呆患者分开是准确的，曲线下面积（AUC）为0.97（根据图4）。AD或FTD患者与其他纳入组的多类别分离在训练集上是良好的（AUC > 0.9），但在当地诊所的134例患者中，准确度大大降低（AD的AUC = 0.76， FTD的AUC = 0.78）。28例轻度认知损伤患者基线时的纵向数据和适当的随访数据均可获得。计算机工具区分渐进式MCI和稳定型MCI的AUC = 0.73，而训练集的AUC = 0.80。临床医生相对较低的准确率（AUC = 0.81）说明了在这一异质队列中预测转化的困难。这是基于核磁共振成像的模式识别方法在常规样本中的首次应用，证明了可行性，但也说明了自动化多类别鉴别诊断必须成为未来方法发展和应用研究的重点
Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC > 0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies