Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung City, Taiwan.
Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Stat Med. 2021 Feb 28;40(5):1243-1261. doi: 10.1002/sim.8838. Epub 2020 Dec 17.
Quantitative imaging biomarkers (QIB) are extracted from medical images in radiomics for a variety of purposes including noninvasive disease detection, cancer monitoring, and precision medicine. The existing methods for QIB extraction tend to be ad hoc and not reproducible. In this article, a general and flexible statistical approach is proposed for handling up to three-dimensional medical images and reasonably capturing features with respect to specific spatial patterns. In particular, a model-based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients. Model fitting and selection are based on maximum likelihood, while feature extractions are via optimal prediction of the underlying true image. Simulation studies are conducted to investigate the properties of the proposed methodology. For illustration, a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint.
定量成像生物标志物（QIB）从放射组学中的医学图像中提取，用于多种目的，包括非侵入性疾病检测、癌症监测和精准医疗。现有的 QIB 提取方法往往是特定的，不可重现的。本文提出了一种通用且灵活的统计方法，用于处理多达三维的医学图像，并合理地捕获与特定空间模式相关的特征。特别是，开发了一种基于模型的空间过程分解，其中随机权重对于个体患者是唯一的，而对于患者共有的分量函数是相同的。模型拟合和选择基于最大似然，而特征提取则通过对潜在真实图像的最佳预测来实现。进行了模拟研究来研究所提出方法的性质。为了说明问题，分析了一个癌症图像数据集，并提取了与临床终点相关的 QIB。
