Department of Clinical Medicine, Guilin Medical university, Guilin, China.
Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang, Autonomous Region, Guilin, China.
Brain Behav. 2023 Nov;13(11):e3222. doi: 10.1002/brb3.3222. Epub 2023 Aug 17.
We aim to develop a radiomics model based on 3-dimensional (3D)-T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter.
In this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D-T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D-T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA).
A total of 108 frontal lobe texture features were extracted from 3D-T1WI images. LASSO selected 58 radiomic features for the final model, including log-sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69-0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value.
Textural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.
我们旨在通过测量额叶白质的变化，基于 3 维（3D）T1WI 图像，建立一个放射组学模型，以区分遗忘型轻度认知障碍（aMCI）患者和正常人群。
本研究纳入了 126 例 aMCI 患者和 174 例正常对照者（NC），均来自当地社区。所有受试者均接受了常规磁共振成像检查（包括 3D-T1WI）。将参与者随机分为训练集（n=242，aMCI：102，NC：140）和测试集（n=58，aMCI：24，NC：34）。从 3D-T1WI 图像中提取额叶纹理特征。采用最小绝对值收缩和选择算子（LASSO）对特征维度进行降维，建立放射组学特征模型。使用受试者工作特征（ROC）曲线分析在训练集和测试集中评估诊断性能。计算 ROC 曲线下面积（AUC）、敏感度和特异度。通过决策曲线分析（DCA）评估放射组学模型区分 aMCI 患者和正常人群的效果。
从 3D-T1WI 图像中共提取了 108 个额叶纹理特征。LASSO 为最终模型选择了 58 个放射组学特征，包括对数标准差（log-sigma）特征（n=18）、原始特征（n=8）和小波特征（n=32）。从 3D T1 成像中提取的放射组学特征区分 aMCI 患者和对照组的表现如下：在训练集，AUC 为 1.00，准确率、敏感度和特异度分别为 100%、98%和 100%。在测试集，AUC 为 0.82（95%CI：0.69-0.95），准确率、敏感度和特异度分别为 69%、92%和 55%。DCA 表明该模型具有良好的临床预测价值。
额叶白质的纹理特征具有区分 aMCI 与正常人群的潜力，这可能成为临床辅助 aMCI 筛查的替代方案。
