融合多种影像标志物，提升阿尔茨海默病早期诊断准确率
Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis
- 整合放射组学、皮层厚度等多类影像特征进行联合分析
- 放射组学特征对早期阿尔茨海默病检测AUC达0.88
- 传统标志物在深度学习时代仍具关键价值，适合临床研究者
阿尔茨海默病（AD）是痴呆的主要原因，早期检测对有效干预至关重要，但现有诊断方法敏感性和特异性常不足。本研究通过提取并整合多种影像生物标志物（包括放射组学、海马纹理特征、皮层厚度测量和深度学习特征），分析来自阿尔茨海默病神经影像计划（ADNI）队列的结构磁共振成像（MRI）扫描，采用综合图像分析与机器学习技术。结果表明，多标志物融合显著提升检测准确性。放射组学与纹理特征成为最有效的早期AD预测因子，对AD和轻度认知障碍（MCI）检测的AUC分别为0.88和0.72。尽管深度学习特征表现不如传统方法，但结合年龄可显著提升MCI检测性能。研究强调，在现代深度学习背景下，经典影像生物标志物仍具重要价值，为早期AD诊断提供稳健框架。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensitivity and specificity. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers, including radiomics, hippocampal texture descriptors, cortical thickness measurements, and deep learning features. We analyze structural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis and machine learning techniques. Our results show that combining multiple biomarkers significantly improves detection accuracy. Radiomics and texture features emerged as the most effective predictors for early AD, achieving AUCs of 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning features proved to be less effective than traditional approaches, incorporating age with other biomarkers notably enhanced MCI detection performance. Additionally, our findings emphasize the continued importance of classical imaging biomarkers in the face of modern deep-learning approaches, providing a robust framework for early AD diagnosis.
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