Song Jiyoung, Hwang Eui Jin, Yoon Soon Ho, Park Chang Min, Goo Jin Mo
From the Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, Korea (J.S., E.J.H., S.H.Y., C.M.P., J.M.G.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (E.J.H., S.H.Y., C.M.P., J.M.G.); Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, Korea (C.M.P.); and Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (J.M.G.).
Invest Radiol. 2025 Mar 20. doi: 10.1097/RLI.0000000000001179.
Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This review summarizes key developments in imaging modalities, computational tools, and clinical applications, highlighting major breakthroughs in thoracic diseases-lung cancer, pulmonary nodules, interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), COVID-19 pneumonia, and pulmonary embolism-and outlining future directions.Artificial intelligence (AI)-driven computer-aided detection systems and radiomic analyses have notably improved the detection and classification of pulmonary nodules, while photon-counting detector CT (PCD-CT) and low-field MRI offer enhanced resolution or radiation-free strategies. For lung cancer, CT texture analysis and perfusion imaging refine prognostication and therapy planning. ILD assessment benefits from automated diagnostic tools and innovative imaging techniques, such as PCD-CT and functional MRI, which reduce the need for invasive diagnostic procedures while improving accuracy. In COPD, dual-energy CT-based ventilation/perfusion assessment and dark-field radiography enable earlier detection and staging of emphysema, complemented by deep learning approaches for improved quantification. COVID-19 research has underscored the clinical utility of chest CT, radiographs, and AI-based algorithms for rapid triage, disease severity evaluation, and follow-up. Furthermore, tuberculosis remains a significant global health concern, highlighting the importance of AI-assisted chest radiography for early detection and management. Meanwhile, advances in CT pulmonary angiography, including dual-energy reconstructions, allow more sensitive detection of pulmonary emboli.Collectively, these innovations demonstrate the power of merging novel imaging technologies, quantitative functional analysis, and AI-driven tools to transform thoracic disease management. Ongoing progress promises more precise and personalized diagnostic and therapeutic strategies for diverse thoracic diseases.
在过去十年中,《放射学研究》发表了大量从根本上推动了胸部成像领域发展的研究。本综述总结了成像模式、计算工具和临床应用方面的关键进展,重点介绍了胸部疾病(肺癌、肺结节、间质性肺疾病(ILD)、慢性阻塞性肺疾病(COPD)、新冠肺炎、肺栓塞)的重大突破,并概述了未来方向。人工智能(AI)驱动的计算机辅助检测系统和放射组学分析显著提高了肺结节的检测和分类能力,而光子计数探测器CT(PCD-CT)和低场MRI提供了更高的分辨率或无辐射策略。对于肺癌,CT纹理分析和灌注成像可优化预后评估和治疗规划。ILD评估受益于自动化诊断工具和创新成像技术,如PCD-CT和功能MRI,这些技术减少了侵入性诊断程序的需求,同时提高了准确性。在COPD中,基于双能CT的通气/灌注评估和暗场射线照相能够更早地检测和分期肺气肿,深度学习方法则可辅助进行更精确的量化。新冠肺炎研究强调了胸部CT、X光片和基于AI的算法在快速分诊、疾病严重程度评估和随访方面的临床实用性。此外,结核病仍然是全球重大的健康问题,凸显了AI辅助胸部X光检查在早期检测和管理中的重要性。同时,CT肺动脉造影的进展,包括双能重建,能够更敏感地检测肺栓塞。
总体而言,这些创新展示了将新型成像技术、定量功能分析和AI驱动工具相结合以变革胸部疾病管理的力量。持续的进展有望为各种胸部疾病带来更精确、个性化的诊断和治疗策略。