^(18)F-FDG PET图像联合可解释的深度学习影像组学模型对原发性帕金森病和非典型性帕金森综合征的鉴别诊断

^(18)F-FDG PET Image Combined with Interpretable Deep Learning Radiomics Model in Differential Diagnosis Between Primary Parkinson's Disease and Atypical Parkinson's Syndrome

目的探究^(18)F-FDG PET图像结合可解释的深度学习影像组学(IDLR)模型在原发性帕金森病(IPD)和非典型性帕金森综合征鉴别诊断中的应用价值。资料与方法本横断面研究纳入2015年3月—2023年2月复旦大学附属华山医院帕金森病PET成像基准数据库330例帕金森病患者的^(18)F-FDG PET图像,其中IPD 211例、进行性核上性麻痹(PSP)59例、多系统萎缩(MSA)60例;包括2个队列(训练组270例和测试组60例)。采集所有受试者的^(18)F-FDG PET图像及临床信息并进行比较。开发一种IDLR提取特征指标,在影像组学特征的监督下从神经网络提取器收集的特征中筛选IDLR特征,并在测试组中构建二分类支持向量机模型,分别计算构建的IDLR模型、传统影像组学模型、标准化摄取值比值模型、深度学习模型在IPD/PSP/MSA组间两两分类的模型性能指标与曲线下面积。采用100次10折交叉验证在2个队列中进行独立分类与测试。通过特征映射展示大脑相关感兴趣区,使用梯度加权类激活图突出大脑中最相关的信息并可视化,检查不同疾病组的模型输出热力图,并将其与临床诊断位置进行比较。结果IDLR模型在不同帕金森综合征患者中分类效果最好,测试组中的曲线下面积(MSA与IPD 0.9357,MSA与PSP 0.9754,IPD与PSP 0.9825)优于其他模型(影像组学模型:Z=1.31~2.96,P均<0.05;标准化摄取值比值模型:Z=1.22~3.23,P均<0.05)。筛选后的IDLR特征映射的影像组学感兴趣区与梯度加权类激活图切片热力图可视化高度一致。结论IDLR模型在^(18)F-FDG PET图像中具备对IPD和非典型性帕金森综合征的鉴别诊断潜力。

Purpose To explore the application value of combining ^(18)F-FDG PET images with interpretable deep learning radiomics(IDLR)models in the differential diagnosis of primary Parkinson's disease(IPD)and atypical Parkinson's syndrome.Materials and Methods This cross-sectional study was conducted using the Parkinson's Disease PET Imaging Benchmark Database from Huashan Hospital,Fudan University from March 2015 to February 2023.A total of 330 Parkinson's disease patients underwent ^(18)F-FDG PET imaging,both ^(18)F-FDG PET imaging and clinical scale information were collected for all subjects.The study included two cohorts,a training group(n=270)and a testing group(n=60),with a total of 211 cases in the IPD group,59 cases in the progressive supranuclear palsy(PSP)group,and a group of 60 patients with multiple system atrophy(MSA).The clinical information between different groups were compared.An IDLR model was developed to extract feature indicators.Under the supervision of radiomics features,IDLR features were selected from the features collected by neural network extractors,and a binary support vector machine model was constructed for the selected features in images of in testing group.The constructed IDLR model,traditional radiomics model and standard uptake ratio model were separately used to calculate the performance metrics and area under curve values of deep learning models for pairwise classification between IPD/PSP/MSA groups.The study conducted independent classification and testing in two cohorts using 10010-fold cross-validation tests.Brain-related regions of interest were displayed through feature mapping,using gradient weighted class activation maps to highlight and visualize the most relevant information in the brain.The output heatmaps of different disease groups were examined and compared with clinical diagnostic locations.Results The IDLR model showed promising results for differentiating between Parkinson's syndrome patients.It achieved the best classification performance and had the highest area under the curve values compared to other comparative models such as the standard uptake ratio model(Z=1.22-3.23,all P<0.05),and radiomics model(Z=1.31-2.96,all P<0.05).The area under the curve values for the IDLR model in differentiating MSA and IPD were 0.9357,for MSA and PSP were 0.9754,for IPD and PSP were 0.9825 in the test set.The IDLR model also showed consistency between its filtered feature maps and the visualization of gradient-weighted class activation mapping slice thermal maps in the radiomics regions of interest.Conclusion The IDLR model has the potential for differential diagnosis between IPD and atypical Parkinson's syndrome in ^(18)F-FDG PET images.