Liao Qinmei, Hu Xihao, Jiang Zhiqiong, Huang Xiaoyun, Guo Jiacheng, Zhu Yuanzhong, He Wenjing
School of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, China.
Department of Gerontology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637002, China.
BMC Med Imaging. 2025 May 22;25(1):181. doi: 10.1186/s12880-025-01732-y.
White matter hyperintensities (WMHs) are closely associated with cognitive frailty (CF). This study aims to explore the potential diagnostic value of WMHs for CF based on radiomics approaches, thereby providing a novel methodology for the early diagnosis and timely intervention of CF.
The present study conducted a retrospective analysis on 147 patients (77 with CF, 70 in the control group). Following an 8:2 ratio, the patients were randomly divided into training and testing sets. Repeated 5-fold cross-validation was adopted for model training and evaluation. Optimal radiomic features were extracted and selected from T2-FLAIR images, and multiple logistic regression analysis was utilized to identify independent risk factors. Three machine learning algorithms-K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM)-were used to construct radiomic models, clinical models, and combined models. The performance of each model in diagnosing CF was evaluated using metrics including the area under the curve (AUC), area under the net benefit curve (AUNBC), and Brier score.
In the test set, the AUC values of KNN, LR, and SVM in the radiomics models were 0.860, 0.916, and 0.885, respectively; the AUC values of the clinical models were 0.868, 0.850, and 0.787, respectively; and the AUC values of the combined models were 0.906, 0.954, and 0.930, respectively. The decision curve analysis (DCA) demonstrated that the combined model was superior to the single models in terms of clinical decision-making efficacy.
The radiomic model, clinical model, and combined model can effectively diagnose CF patients, with the combined model demonstrating the best diagnostic efficacy.
Not applicable.
白质高信号（WMHs）与认知衰弱（CF）密切相关。本研究旨在基于放射组学方法探索WMHs对CF的潜在诊断价值，从而为CF的早期诊断和及时干预提供一种新方法。
本研究对147例患者（77例CF患者，70例为对照组）进行回顾性分析。按照8:2的比例，将患者随机分为训练集和测试集。采用重复5折交叉验证进行模型训练和评估。从T2-FLAIR图像中提取并选择最佳放射组学特征，并利用多元逻辑回归分析确定独立危险因素。使用三种机器学习算法——K近邻（KNN）、逻辑回归（LR）和支持向量机（SVM）——构建放射组学模型、临床模型和联合模型。使用包括曲线下面积（AUC）、净效益曲线下面积（AUNBC）和Brier评分在内的指标评估每个模型在诊断CF方面的性能。
在测试集中，放射组学模型中KNN、LR和SVM的AUC值分别为0.860、0.916和0.885；临床模型的AUC值分别为0.868、0.850和0.787；联合模型的AUC值分别为0.906、0.954和0.930。决策曲线分析（DCA）表明，联合模型在临床决策效能方面优于单一模型。
放射组学模型、临床模型和联合模型均可有效诊断CF患者，联合模型的诊断效能最佳。
不适用。
