Kippenhan J S, Barker W W, Pascal S, Nagel J, Duara R
Wien Center for Memory Disorder, Mt. Sinai Medical Center, Miami Beach, Florida 33140.
J Nucl Med. 1992 Aug;33(8):1459-67.
The value of PET as an objective diagnostic tool for dementia may depend on the degree to which abnormal metabolic patterns can be detected by quantitative classification methods. In these studies, a neural-network classifier based on coarse region of interest analyses was used to classify normal and abnormal FDG-PET scans. The performance of neural networks and of an expert reader were evaluated by cross-validation testing. When the "abnormal" class was represented by subjects with clinical diagnoses of "Probable Alzheimer's," the areas under the relative-operating-characteristic (ROC) curves were 0.85 and 0.89 for the neural network and the expert reader, respectively. When testing with abnormal subjects represented by "Possible AD" cases, ROC areas for both the network and the expert were 0.81. The neural network out-performed discriminant analysis. It is concluded that PET has potential for the detection of abnormal brain function in dementing diseases, and that the combination of neural networks and PET is a useful diagnostic tool. Despite the low-resolution "view" afforded the neural network, its performance was nearly equivalent to that of an expert reader.
正电子发射断层扫描（PET）作为痴呆症客观诊断工具的价值，可能取决于定量分类方法能够检测到异常代谢模式的程度。在这些研究中，基于粗略感兴趣区域分析的神经网络分类器被用于对正常和异常的氟代脱氧葡萄糖（FDG）-PET扫描进行分类。神经网络和专业阅片者的表现通过交叉验证测试进行评估。当“异常”类别由临床诊断为“可能的阿尔茨海默病”的受试者代表时，神经网络和专业阅片者的相对操作特征（ROC）曲线下面积分别为0.85和0.89。当以“可能的阿尔茨海默病（AD）”病例代表的异常受试者进行测试时，网络和专业阅片者的ROC面积均为0.81。神经网络的表现优于判别分析。得出的结论是，PET在检测痴呆疾病中的异常脑功能方面具有潜力，并且神经网络与PET的结合是一种有用的诊断工具。尽管神经网络的“视野”分辨率较低，但其表现几乎与专业阅片者相当。
