Application of data mining technology to diagnostic imaging support of dementia using FDG-PET.

本工作的目的是初步构建FDG-PET图像对痴呆的诊断支持系统。采用23名日本健康志愿者和58名MCI患者的FDG-PET图像。在每张FDG图像上自动放置116个roi，并使用区域与全脑摄取比进行二级分类。采用支持向量机和k指数法进行分类，ROC分析曲线下面积分别为71.1%和74.3%。仅考虑MCI与正常之间的临床诊断准确性较低，从图片上看，这似乎是一个很好的结果。在一些癌症患者的脑FDG- pet图像中，观察到大脑皮层FDG摄取与患者体重呈负相关。

The purpose of the present work was preliminary construction of diagnosis support system of dementia using FDG-PET image. FDG-PET images of Japanese 23 healthy volunteers and 58 MCI patients were used. 116 ROIs were automatically placed on each FDG image and region to whole brain uptake ratio was used for 2-class clasification. Support Vector Machine and k-index method, the reserch leader original, were used for the classification and area under the curve of ROC analysis was 71.1% and 74.3% respectively. It was seemed to be good results with pictures only consider the low accuracy of clinical diagnosis between MCI and normal. The inverse correlation was observed in FDG uptake in the cerebral cortex and the patient's body weight in the brain FDG-PET images of cancer a number of patients.