The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Medical Physics Unit, McGill University, Montreal, Quebec, Canada; Department of Radiation Oncology, University of California - San Francisco, San Francisco, United States.
Semin Nucl Med. 2019 Sep;49(5):438-449. doi: 10.1053/j.semnuclmed.2019.06.005. Epub 2019 Jun 20.
Radiomics - the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.
放射组学——从医学成像模式中提取的定量图像特征的高通量计算——可用于辅助临床决策支持系统,以构建诊断、预后和预测模型,从而最终根据个体特征实现个性化管理。目前有各种用于提取放射组学特征的工具,该领域在标准化和验证方面取得了显著的科学进展。与目前使用的参数相比,分子成像的放射组学分析有望提供更全面的组织描述。在此,我们回顾了放射组学的工作流程、该领域目前面临的挑战及其纳入临床决策支持系统的潜力,以最大限度地描述疾病特征,并改善临床决策。我们还提出了放射组学标准化和实施的指南,以促进其向临床应用的转变。