Hou Chuanke, Zhang Meng, Jiang Xingyuan, Li Hongjun
Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Department of Neuro-oncology Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):7989-8007. doi: 10.21037/qims-2025-290. Epub 2025 Aug 19.
People living with human immunodeficiency virus (PLWH) are at risk of human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND). The mildest disease stage of HAND is asymptomatic neurocognitive impairment (ANI), and the accurate diagnosis of this stage can facilitate timely clinical interventions. The aim of this study was to mine features related to the diagnosis of ANI based on resting-state functional magnetic resonance imaging (rs-fMRI) and to establish classification models.
A total of 74 patients with 74 ANI and 78 with PLWH but no neurocognitive disorders (PWND) were enrolled. Basic clinical, T1-weighted imaging, and rs-fMRI data were obtained. The rs-fMRI signal values and radiomics features of 116 brain regions designated by the Anatomical Automatic Labeling template were collected, and the features were selected via the least absolute shrinkage and selection operator. rs-fMRI, radiomics, and combined models were constructed with five machine learning classifiers, respectively. Model performance was evaluated via the mean area under the curve (AUC), accuracy, sensitivity, and specificity.
Twenty-one rs-fMRI signal values and 28 radiomics features were selected to construct models. The performance of the combined models was exceptional, with the standout random forest (RF) model delivering an AUC value of 0.902 [95% confidence interval (CI): 0.813-0.990] in the validation set and 1.000 (95% CI: 1.000-1.000) in the training set. Further analysis of the 49 features revealed significantly overlapping brain regions for both feature types. Three key features demonstrating significant differences between ANI and PWND were identified (all P values <0.001). These features correlated with cognitive test performance (r>0.3).
The RF combined model exhibited high classification performance in ANI, enabling objective and reliable individual diagnosis in clinical practice. It thus represents a novel method for characterizing the brain functional impairment and pathophysiology of patients with ANI. Greater attention should be paid to the frontoparietal and striatum in the research and clinical work related to ANI.
人类免疫缺陷病毒（HIV）感染者有患与HIV相关的神经认知障碍（HAND）的风险。HAND最轻微的疾病阶段是无症状神经认知损害（ANI），准确诊断这一阶段有助于及时进行临床干预。本研究的目的是基于静息态功能磁共振成像（rs-fMRI）挖掘与ANI诊断相关的特征，并建立分类模型。
共纳入74例ANI患者和78例HIV感染者但无神经认知障碍（PWND）的患者。获取基本临床、T1加权成像和rs-fMRI数据。收集由解剖自动标记模板指定的116个脑区的rs-fMRI信号值和影像组学特征，并通过最小绝对收缩和选择算子选择特征。分别用五种机器学习分类器构建rs-fMRI、影像组学和联合模型。通过平均曲线下面积（AUC）、准确性、敏感性和特异性评估模型性能。
选择21个rs-fMRI信号值和28个影像组学特征构建模型。联合模型的性能优异，突出的随机森林（RF）模型在验证集中的AUC值为0.902[95%置信区间（CI）：0.813-0.990]，在训练集中为1.000（95%CI：1.000-1.000）。对这49个特征的进一步分析显示，两种特征类型的脑区有明显重叠。确定了三个在ANI和PWND之间表现出显著差异的关键特征（所有P值<0.001）。这些特征与认知测试表现相关（r>0.3）。
RF联合模型在ANI中表现出较高的分类性能，能够在临床实践中实现客观可靠的个体诊断。因此，它代表了一种表征ANI患者脑功能损害和病理生理学的新方法。在与ANI相关的研究和临床工作中，应更加关注额顶叶和纹状体。
