The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static features.
翻译:放射学的特征提取方法主要基于某一时刻的静态成像图象,而疾病的发生和发展是一个动态过程,不能仅以静态特性充分反映。本研究提出一个新的动态放射学特征提取工作流程,使用同一病人的根据时间而定的成像图象,侧重于图像特征随时间变化,然后将其量化为诊断或预测性评估的新动态特征。我们首先确定动态放射学的数学范式,并采用三种具体方法来描述特征的转化过程。使用三种不同的临床问题来验证传统的2D和3D静态特征的拟议动态特征的性能。