Magnin Cheryl Y, Lauer David, Ammeter Michael, Gote-Schniering Janine
Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Lung Precision Medicine (LPM), Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland.
Breathe (Sheff). 2025 Mar 18;21(1):230225. doi: 10.1183/20734735.0225-2023. eCollection 2025 Jan.
Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents a significant advancement in clinical lung imaging, offering a powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture and wavelet characteristics from medical images that can uncover detailed and often subtle information that goes beyond the visual capabilities of radiological examiners. By extracting this quantitative information, radiomics can provide deep insights into the pathophysiology of lung diseases and support clinical decision-making as well as personalised medicine approaches. In this educational review, we provide a step-by-step guide to radiomics-based medical image analysis, discussing the technical challenges and pitfalls, and outline the potential clinical applications of radiomics in diagnosing, prognosticating and evaluating treatment responses in respiratory medicine.
放射影像学是肺部疾病临床检查的基石。放射组学是临床肺部成像的一项重大进展，为补充传统的定性图像分析提供了强大工具。放射组学特征是定量的，通过计算描述医学图像的形状、强度、纹理和小波特征，能够揭示超出放射科医生视觉能力的详细且往往细微的信息。通过提取这些定量信息，放射组学可以深入洞察肺部疾病的病理生理学，并支持临床决策以及个性化医疗方法。在本教育性综述中，我们提供了基于放射组学的医学图像分析的分步指南，讨论技术挑战和陷阱，并概述放射组学在呼吸医学的诊断、预后评估和治疗反应评估中的潜在临床应用。
