Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland.
J Alzheimers Dis. 2023;92(3):941-957. doi: 10.3233/JAD-220806.
Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process.
Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine.
The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model.
The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis.
The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
在临床实践中，早期阿尔茨海默病（AD）的检测仍然存在问题。本研究旨在寻找基于 T1 加权 MRI 的 AD 和轻度认知障碍（MCI）标志物，以改善筛查过程。
我们假设构建一个易于医生在日常临床实践中使用的筛查模型。
使用多项逻辑回归检测状态：AD、MCI 和正常对照（NC），并结合贝叶斯信息准则进行模型选择。在预测模型中考虑了几个基于 T1 加权 MRI 的放射组学特征作为解释变量。
最佳放射组学预测因子是相对脑容量。所提出的方法通过实现 AD 与 NC 分类的平衡准确率为 95.18%、AUC 为 93.25%、NPV 为 97.93%和 PPV 为 90.48%，证明了其质量。将两种模型进行比较：一种是仅将 MMSE 评分作为独立变量，另一种是校正相对脑值和年龄后的模型，结果表明，添加基于 T1 加权 MRI 的生物标志物可提高 MCI 检测的质量（AUC：67.04%比 71.08%），同时保持 AD 的质量（AUC：93.35%比 93.25%）。此外，在与原始诊断不一致的被预测为 AD 的 MCI 患者中，ADNI 中有 60%和 EDSD 中有 76.47%在 48 个月的随访中被重新诊断为 AD。这表明我们的模型可以比标准医学诊断更早地检测到 AD 患者。
所创建的方法是非侵入性的、廉价的、临床可行的，并且能够有效地支持 AD/MCI 筛查。
