Zeng Rong, Yang Beisheng, Wu Faqi, Liu Huan, Wu Xiaojia, Tang Lin, Song Rao, Zheng Qingqing, Wang Xia, Guo Dajing
Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Neurol. 2025 Mar 12;16:1552940. doi: 10.3389/fneur.2025.1552940. eCollection 2025.
Accurately predicting the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is a challenging task, which is crucial for helping develop personalized treatment plans to improve prognosis.
To develop new technology for the early prediction of AD using artificial intelligence and cortical features on MRI.
A total of 162 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. By using a 3D-MPRAGE sequence, T1W images for each patient were acquired. All patients were randomly divided into a training set ( = 112) and a validation set ( = 50) at a ratio of 7:3. Morphological features of the cerebral cortex were extracted with FreeSurfer software. Network features were extracted from gray matter with the GRETNA toolbox. The network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index).
In the training group, the C-indexes of the network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were 0.834, 0.926, 0.915, 0.949, 0.928, and 0.951, respectively. The C-indexes of those models in the validation group were 0.765, 0.784, 0.849, 0.877, 0.884, and 0.880, respectively. The morphology-network-clinical model performed the best. A multi-predictor nomogram with high accuracy for individual AD prediction (C-index = 0.951) was established.
The early occurrence of AD could be accurately predicted using our morphology-network-clinical model and the multi-predictor nomogram. This could help doctors make early and personalized treatment decisions in clinical practice, which showed important clinical significance.
准确预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的进展是一项具有挑战性的任务，这对于制定个性化治疗方案以改善预后至关重要。
利用人工智能和MRI上的皮质特征开发AD早期预测的新技术。
从阿尔茨海默病神经影像倡议（ADNI）数据库中纳入162例MCI患者。使用3D-MPRAGE序列，获取每位患者的T1W图像。所有患者按7:3的比例随机分为训练集（n = 112）和验证集（n = 50）。使用FreeSurfer软件提取大脑皮质的形态学特征。使用GRETNA工具箱从灰质中提取网络特征。通过多变量Cox比例风险模型建立网络、形态学、网络 - 临床、形态学 - 临床、形态学 - 网络和形态学 - 网络 - 临床模型。通过一致性指数（C指数）评估每个模型的性能。
在训练组中，网络、形态学、网络 - 临床、形态学 - 临床、形态学 - 网络和形态学 - 网络 - 临床模型的C指数分别为0.834、0.926、0.915、0.949、0.928和0.951。这些模型在验证组中的C指数分别为0.765、0.784、0.849、0.877、0.884和0.880。形态学 - 网络 - 临床模型表现最佳。建立了用于个体AD预测的高精度多预测列线图（C指数 = 0.951）。
使用我们的形态学 - 网络 - 临床模型和多预测列线图可以准确预测AD的早期发生。这有助于医生在临床实践中做出早期和个性化的治疗决策，具有重要的临床意义。
