Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline

IF 3.3 2区 医学 Q1 NEUROIMAGING
Qinjie Li, Liang Cui, Yihui Guan, Yuehua Li, Fang Xie, Qihao Guo
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Abstract

There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four individuals with subjective cognitive decline (SCD) were enrolled in this study. All subjects completed neuropsychological assessments and underwent 18F-florbetapir PET, structural MRI, and functional MRI. A total of 315 features were extracted from the MRI, demographics, and neuropsychological scales and selected using the least absolute shrinkage and selection operator (LASSO). The logistic regression (LR) model, based on machine learning, was trained to classify SCD as either β-amyloid (Aβ) positive or negative. A nomogram was established using a multivariate LR model to predict the risk of Aβ+. The performance of the prediction model and nomogram was assessed with area under the curve (AUC) and calibration. The final model was based on the right rostral anterior cingulate thickness, the grey matter volume of the right inferior temporal, the ReHo of the left posterior cingulate gyrus and right superior temporal gyrus, as well as MoCA-B and AVLT-R. In the training set, the model achieved a good AUC of 0.78 for predicting Aβ+, with an accuracy of 0.72. The validation of the model also yielded a favorable discriminatory ability with an AUC of 0.88 and an accuracy of 0.83. We have established and validated a model based on cognitive, sMRI, and fMRI data that exhibits adequate discrimination. This model has the potential to predict amyloid status in the SCD group and provide a noninvasive, cost-effective way that might facilitate early screening, clinical diagnosis, and drug clinical trials.

Abstract Image

使用临床和MRI特征预测主观认知衰退个体淀粉样蛋白阳性的模型和Nomogram
迫切需要使用无创和可及的指标来准确预测脑淀粉样变性,以促进临床前阶段阿尔茨海默病(AD)个体的早期诊断。244名主观认知衰退(SCD)患者参加了这项研究。所有受试者都完成了神经心理学评估,并接受了18F-florbetapir PET、结构MRI和功能MRI检查。从MRI、人口统计学和神经心理学量表中提取了315个特征,并使用最小绝对收缩和选择算子(LASSO)进行选择。训练基于机器学习的逻辑回归(LR)模型,将SCD分类为β-淀粉样蛋白(Aβ)阳性或阴性。采用多变量LR模型建立了预测Aβ+风险的nomogram。用曲线下面积(AUC)和标定对预测模型和nomogram的性能进行了评价。最终的模型是基于右侧扣带前喙部厚度、右侧颞下回灰质体积、左侧扣带后回和右侧颞上回ReHo以及MoCA-B和AVLT-R。在训练集中,该模型预测a β+的AUC为0.78,准确率为0.72。模型的验证也产生了良好的区分能力,AUC为0.88,准确率为0.83。我们已经建立并验证了一个基于认知、sMRI和fMRI数据的模型,该模型显示出足够的辨别能力。该模型有可能预测SCD组的淀粉样蛋白状态,并提供一种无创、经济有效的方法,可能有助于早期筛查、临床诊断和药物临床试验。
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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
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