Lin Shuai, Xue Ming, Sun Jiali, Xu Chang, Wang Tianqi, Lian Jianxiu, Lv Min, Yang Ping, Sheng Chenjun, Cheng Zijian, Wang Wei
Department of MRI, First Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of Radiology, First Affiliated Hospital of Harbin Medical University, Harbin, China.
Acad Radiol. 2025 Feb;32(2):951-962. doi: 10.1016/j.acra.2024.08.059. Epub 2024 Sep 26.
Accurate prediction of the progression of preclinical Alzheimer's disease (AD) is crucial for improving clinical management and disease prognosis. The objective of this study was to develop and validate clinical-radimoics integrated model to predict the time to progression (TTP) and disease risk stratification of preclinical AD.
A total of 244 cases (mean age: 73.8 ± 5.5 years, 120 women) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly divided into the training cohort (n = 172) and validation cohort (n = 72) using a 7:3 ratio. Clinical factors were identified by univariate and multivariate COX regression. Radiomics features were extracted from GM, WM and CSF of TWI images and selected by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO). Using selected clinical factors and radiomics features, the clinical, radimocis and clinical-radiomics nomogram models were developed for predicting the TTP. The performance of each model was assessed by C-index. The risk stratification ability and predicting efficacy of the clinical-radiomics model were utilizing the Kaplan-Meier curve and receiver operator characteristic (ROC) curve.
The C-index of clinical, radimocis and clinical-radiomics models were 0.852 (95% confidence interval[CI]:0.810-0.893), 0.863 (95%CI:0.816-0.910) and 0.903 (95%:0.870-0.936) in the training cohort and 0.725 (95%CI:0.630-0.820), 0.788 (95%CI:0.678-0.898), 0.813(95%CI:0.734-0.892) in the validation cohort. The AUCs of the multi-predictor nomogram at 1-, 3-, 5- and 7-year were 0.894, 0.908, 0.930, 0.979 in the training cohort and 0.671, 0.726, 0.839, 0.931 in the validation cohort.
In this study, we constructed a clinical-radimoics integrated model to predict the progression of preclinical AD and stratified the risk of disease progression in preclinical AD.
准确预测临床前阿尔茨海默病（AD）的进展对于改善临床管理和疾病预后至关重要。本研究的目的是开发并验证一种临床-影像组学综合模型，以预测临床前AD的进展时间（TTP）和疾病风险分层。
从阿尔茨海默病神经影像倡议（ADNI）数据库中选取244例患者（平均年龄：73.8±5.5岁，女性120例），按照7:3的比例随机分为训练队列（n = 172）和验证队列（n = 72）。通过单因素和多因素COX回归确定临床因素。从TWI图像的灰质（GM）、白质（WM）和脑脊液（CSF）中提取影像组学特征，并通过Spearman相关性分析和最小绝对收缩和选择算子（LASSO）进行选择。利用选定的临床因素和影像组学特征，开发用于预测TTP的临床、影像组学和临床-影像组学列线图模型。通过C指数评估每个模型的性能。利用Kaplan-Meier曲线和受试者工作特征（ROC）曲线评估临床-影像组学模型的风险分层能力和预测效能。
训练队列中临床、影像组学和临床-影像组学模型的C指数分别为0.852（95%置信区间[CI]：0.810 - 0.893）、0.863（95%CI：0.816 - 0.910）和0.903（95%：0.870 - 0.936），验证队列中分别为0.725（95%CI：0.630 - 0.820）、0.788（95%CI：0.678 - 0.898）、0.813（95%CI：0.734 - 0.892）。训练队列中多预测因子列线图在1年、3年、5年和7年时的曲线下面积（AUC）分别为0.894、0.908、0.930、0.979，验证队列中分别为0.671、0.726、0.839、0.931。
在本研究中，我们构建了一个临床-影像组学综合模型来预测临床前AD的进展，并对临床前AD的疾病进展风险进行分层。
