■基于体素的形态计量学(VBM),基于表面的形态计量学(SBM),和影像组学广泛应用于神经图像分析领域,虽然目前尚不清楚传统形态计量学和新兴的影像组学方法在诊断脑衰老方面的性能比较。在这项研究中,我们的目标是开发一个VBM-SBM模型和一个基于认知正常(CN)个体的脑老化的影像组学模型,并比较他们的表现,以探索这两种方法的优势,弱点,和关系。
■967名CN参与者被纳入本研究。根据年龄66分为中年组(n=302)和老年组(n=665)。来自阿尔茨海默病神经影像学计划的360名受试者的数据用于VBM-SBM和放射组学模型的训练和内部测试,以及来自澳大利亚成像的607名受试者的数据,生物标志物和生活方式,国家老年痴呆症协调中心,和帕金森进展标志物倡议数据库用于外部测试。物流回归参与了这两个模型的构建。接收器工作特性曲线下的面积(AUC),灵敏度,特异性,准确度,正预测值,和阴性预测值用于评估两个模型的性能。DeLong检验用于比较模型之间AUC的差异。采用Spearman相关分析观察年龄、VBM-SBM参数,和影像组学特征。
■VBM-SBM模型和影像组学模型的AUC在训练集中分别为0.697和0.778(p=0.018),内部测试集中的0.640和0.789(p=0.007),AIBL测试集中的0.736和0.737(p=0.972),NACC测试集中的0.746和0.838(p<0.001),PPMI测试集中的0.701和0.830(p=0.036)。在VBM-SBM参数和影像组学特征之间观察到弱相关性(p<0.05)。
■影像组学模型比VBM-SBM模型获得了更好的性能。Radiomics为优先考虑性能和概括的研究人员提供了一个很好的选择,而VBM-SBM更适合那些强调可解释性和临床实践的人。
UNASSIGNED: Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods\' strengths, weaknesses, and relationships.
UNASSIGNED: 967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer\'s Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer\'s Coordinating Center, and the Parkinson\'s Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features.
UNASSIGNED: The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05).
UNASSIGNED: The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.