Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.
Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.
复制标题
使用临床和多模式磁共振成像数据对HIV患者神经认知损害的机器学习预测。
DOI：
10.1007/s13365-020-00930-4
复制
发表时间：
2021-03
影响因子：
3.2
通讯作者：
Meade CS
中科院分区：
文献类型：
--
作者：
Xu Y;Lin Y;Bell RP;Towe SL;Pearson JM;Nadeem T;Chan C;Meade CS
hiv相关神经认知障碍（NCI）的诊断仍然是一个临床挑战。本研究的目的是利用临床和磁共振成像（MRI）衍生的特征，建立HIV感染者NCI的预测模型。样本包括101名患有慢性艾滋病的成年人。NCI是通过由七个领域组成的标准化神经心理学测试来确定的。MRI特征包括高分辨率解剖扫描的灰质体积和弥散加权成像的白质完整性。临床特征包括人口统计学、药物使用和常规实验室检查。使用最小绝对收缩和选择算子逻辑回归对MRI特征进行变量选择。这些特征随后被用于训练支持向量机（SVM）来预测NCI。进行了三种不同的分类任务：一种只使用临床特征；第二种仅使用选定的MRI特征；第三组同时使用临床和选定的MRI特征。通过受试者工作特征曲线下面积（AUC）、准确性、敏感性和特异性评估模型的性能，并进行10倍交叉验证。将选定的MRI与临床特征相结合的SVM分类器优于单独使用临床特征或MRI特征的模型（AUC: 0.83 vs. 0.62 vs. 0.79；准确率：0.80 vs. 0.65 vs. 0.72；灵敏度：0.86 vs. 0.85 vs. 0.86；特异性：0.71 vs. 0.37 vs. 0.52）。我们的研究结果提供了初步证据，表明结合临床和MRI特征可以提高NCI预测的准确性，并可以作为HIV临床实践中NCI诊断的潜在工具。
Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.
登录
查看更多内容
DOI：
10.1076/jcen.25.4.571.13876
发表时间：
2003-06-01
影响因子：
2.2
作者：
Diehr, MC;Cherner, M;Heaton, RK
通讯作者：
Heaton, RK
影响因子：
5.7
作者：
Adeli E;Kwon D;Zhao Q;Pfefferbaum A;Zahr NM;Sullivan EV;Pohl KM
通讯作者：
Pohl KM
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
DOI：
10.1080/13803390490510031
发表时间：
2004-05-01
影响因子：
2.2
作者：
Carey, CL;Woods, SP;Heaton, RK
通讯作者：
Heaton, RK
影响因子：
5.7
作者：
Abdulkadir, Ahmed;Mortamet, Benedicte;Vemuri, Prashanthi;Jack, Clifford R., Jr.;Krueger, Gunnar;Kloeppel, Stefan
通讯作者：
Kloeppel, Stefan