Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease
Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease
复制标题
双模型放射组学生物标志物预测轻度认知障碍进展为阿尔茨海默病
DOI：
10.3389/fnins.2018.01045
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发表时间：
2019-01-11
影响因子：
4.3
通讯作者：
Fernandez, Dariella
中科院分区：
文献类型：
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作者：
Zhou, Hucheng;Jiang, Jiehui;Fernandez, Dariella
预测轻度认知障碍（MCI）发展为阿尔茨海默病（AD）具有重要的临床意义。在这项研究中，我们提出了一种双模型放射学分析，采用多变量Cox比例风险回归模型来研究与MCI转化为AD相关的有希望的危险因素。来自AD神经成像倡议数据库的T1结构磁共振成像（MRI）和f -18氟脱氧葡萄糖（FDG）正电子发射断层扫描（PET）数据收集了131例3年内转化为AD的MCI患者和132例3年内未转化的MCI患者。将这些被试随机分成70%的训练集和30%的测试集。采用小波变换对MRI和PET图像进行融合。在受试者的子集中，使用双样本t检验进行组比较，以确定与MCI转换相关的兴趣区域（roi）。使用已发布的放射组学工具，从每个个体的roi中建立172个放射学特征。最后，构建l1惩罚的Cox模型，并采用Harrell’s C指数（C-index）评价模型的预测精度。为了评估我们提出的方法的有效性，我们使用相同的分析框架分别评估MRI和PET数据。我们使用临床数据、MRI图像、PET图像、融合MRI/PET图像以及临床变量和融合MRI/PET图像组合构建了预后Cox模型。实验结果表明，捕获的roi与向AD的转化显著相关，如双侧海马灰质萎缩和颞顶皮质代谢低下。与临床模型相比，成像模型（MRI/PET/融合）在预测转化方面有显著增强，尤其是融合模式的Cox模型。此外，融合模式成像和临床变量的结合导致预测的准确性最高。测试数据集中临床/MRI/PET/融合/联合模型的平均c指数分别为0.69、0.73、0.73、0.75和0.78。这些结果表明，放射组学分析和Cox模型分析的结合可以成功地用于生存分析，并可能成为个性化精准医疗患者从MCI转变为AD的有力工具。
Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and F-18-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.