ObjectiveVoxel-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.Methods967 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.ResultsThe 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).ConclusionThe 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.

中文翻译：

---

形态测量学与放射组学的比较：基于灰质检测正常的大脑衰老

目的基于体素的形态测量（VBM）、基于表面的形态测量（SBM）和放射组学在神经图像分析领域得到广泛应用，但传统形态测量与新兴放射组学方法在诊断脑衰老方面的性能比较仍不清楚。在本研究中，我们旨在开发基于认知正常 (CN) 个体的 VBM-SBM 模型和脑衰老放射组学模型，并比较它们的性能，以探讨两种方法的优点、缺点和关系。方法 967 名 CN 参与者被纳入这项研究。受试者被分为中年组（n= 302）和老年组（n= 665）根据66岁。来自阿尔茨海默病神经影像计划的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、生物标记和放射组学模型的607名受试者的数据外部测试使用了生活方式、国家阿尔茨海默病协调中心和帕金森病进展标志物倡议数据库。逻辑回归参与了这两个模型的构建。使用受试者工作特征曲线下面积（AUC）、敏感性、特异性、准确性、阳性​​预测值和阴性预测值来评估两种模型的性能。 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参数与影像组学特征之间的相关性。结果VBM-SBM模型和影像组学模型在训练集中的AUC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），以及 PPMI 测试集中的 0.701 和 0.830（p= 0.036）。 VBM-SBM 参数和放射组学特征之间观察到弱相关性（p< 0.05).结论放射组学模型比VBM-SBM模型取得了更好的性能。放射组学为优先考虑性能和泛化性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研究人员。