Institute of Radiology, University Medical Center Erlangen, Germany.
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States.
Rofo. 2024 Apr;196(4):354-362. doi: 10.1055/a-2175-4446. Epub 2023 Nov 9.
Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).
This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.
Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.
· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..
· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.
影像学标志物是通过无创方式采集的影像学手段的定量参数,可用于推断生理和病理生理过程,并可能由单个(单参数)或多个参数(双参数或多参数)组成。
本综述旨在介绍多模态和多参数影像学标志物定量的最新技术。在此,将讨论人工智能在生物标志物中的应用,并解释影像学标志物在乳腺癌和前列腺癌中的临床应用。为了准备这篇综述文章,我们在 Pubmed、Web of Science 和 Google Scholar 上进行了广泛的文献检索。对结果进行了评估和讨论,以确保其一致性和通用性。
不同的影像学标志物(多参数)是基于放射学、核医学或混合成像等互补影像学手段(多模态)的使用进行定量的。从这些技术中,可以确定形态学(例如,大小)、功能(例如,血管生成或扩散)、代谢(例如,葡萄糖代谢)或分子(例如,前列腺特异性膜抗原 PSMA 的表达)水平的参数。越来越多的是使用人工智能,通过机器学习算法来整合和加权影像学标志物。通过这种方式,影像学标志物的临床应用正在增加,如乳腺癌和前列腺癌的诊断。
影像学标志物是用于检测生理和病理生理过程的定量参数。
多模态和多参数影像学的影像学标志物通过人工智能算法进行整合。
定量影像学参数是所有肿瘤实体(如乳腺癌和前列腺癌)诊断的基本组成部分。
Bäuerle T, Dietzel M, Pinker K, et al.Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024;196:354-362.