Department of Radiology of the First Affiliated Hospital, University of South China, Hengyang, 421001, China.
School of Cyberspace Security, Guangzhou University, Guangzhou, 510006, China.
J Imaging Inform Med. 2024 Aug;37(4):1261-1272. doi: 10.1007/s10278-024-01060-7. Epub 2024 Mar 1.
Lenticulostriate arteries (LSA) are potentially valuable for studying vascular cognitive impairment. This study aims to investigate correlations between cognitive impairment and LSA through clinical and radiomics features analysis. We retrospectively included 102 patients (mean age 62.5±10.3 years, 60 males), including 58 with mild cognitive impairment (MCI) and 44 with moderate or severe cognitive impairment (MSCI). The MRI images of these patients were subjected to z-score preprocessing, manual regions of interest (ROI) outlining, feature extraction (pyradiomics), feature selection [max-relevance and min-redundancy (mRMR), least absolute shrinkage and selection operator (LASSO), and univariate analysis], model construction (multivariate logistic regression), and evaluation [receiver operating characteristic curve (ROC), decision curve analysis (DCA), and calibration curves (CC)]. In the training dataset (71 patients, 44 MCI) and the test dataset (31 patients, 17 MCI), the area under curve (AUC) of the combined model (training 0.88 [95% CI 0.78, 0.97], test 0.76 [95% CI 0.6, 0.93]) was better than that of the clinical model and the radiomics model. The DCA results demonstrated the highest net yield of the combined model relative to the clinical and radiomics models. In addition, we found that LSA total vessel count (0.79 [95% CI 0.08, 1.59], P = 0.038) and wavelet.HLH_glcm_MCC (-1.2 [95% CI -2.2, -0.4], P = 0.008) were independent predictors of MCI. The model that combines clinical and radiomics features of LSA can predict MCI. Besides, LSA vascular parameters may serve as imaging biomarkers of cognitive impairment.
纹状体动脉（LSA）在研究血管性认知障碍方面具有潜在的价值。本研究旨在通过临床和放射组学特征分析，研究认知障碍与 LSA 之间的相关性。我们回顾性纳入了 102 名患者（平均年龄 62.5±10.3 岁，60 名男性），包括 58 名轻度认知障碍（MCI）患者和 44 名中重度认知障碍（MSCI）患者。对这些患者的 MRI 图像进行 z 分数预处理、手动感兴趣区（ROI）勾画、特征提取（pyradiomics）、特征选择（最大相关性最小冗余度（mRMR）、最小绝对收缩和选择算子（LASSO）、单变量分析）、模型构建（多变量逻辑回归）和评估（接受者操作特征曲线（ROC）、决策曲线分析（DCA）和校准曲线（CC））。在训练数据集（71 例患者，44 例 MCI）和测试数据集（31 例患者，17 例 MCI）中，联合模型（训练 0.88 [95%CI 0.78, 0.97]，测试 0.76 [95%CI 0.6, 0.93]）的曲线下面积（AUC）优于临床模型和放射组学模型。DCA 结果表明，与临床和放射组学模型相比，联合模型的净收益最高。此外，我们发现 LSA 总血管计数（0.79 [95%CI 0.08, 1.59]，P = 0.038）和小波.HLH_glcm_MCC（-1.2 [95%CI -2.2, -0.4]，P = 0.008）是 MCI 的独立预测因子。联合 LSA 临床和放射组学特征的模型可预测 MCI。此外，LSA 血管参数可能是认知障碍的影像学生物标志物。
