Quantitative Health Sciences /JJN3, Cleveland Clinic Foundation, 9500 Euclid Ave. Cleveland, OH 44195.
Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Huang, Rockville, Maryland.
Acad Radiol. 2023 Feb;30(2):147-158. doi: 10.1016/j.acra.2022.08.031. Epub 2022 Sep 27.
Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.
多参数定量成像结合了解剖学、功能和/或行为生物标志物，用于描述组织、检测疾病、识别表型、定义纵向变化或预测结果。多个成像参数有时会分别考虑，但理想情况下是集体评估。通常，它们被转换为李克特解释，忽略了可能导致更好的可重复性或结果预测的定量特性的相关性。在本文中，我们提出了多参数定量成像的三个用例：i）多维描述符，ii）表型分类，和 iii）风险预测。还提出了基于放射组学数据驱动标志物的第四个应用。我们描述了所有用例共有的技术性能特征及其度量标准，并为多参数定量成像的开发、估计和测试提供了一个结构。本文是关于四个应用的一系列单独文章的概述，为医学中的多参数成像应用提供了统计框架。
