Division of Thoracic Imaging, Mayo Clinic, Rochester, MN.
J Thorac Imaging. 2023 Nov 1;38(Suppl 1):S7-S18. doi: 10.1097/RTI.0000000000000705. Epub 2023 Mar 22.
Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.
间质性肺疾病(ILD)是一组异质性疾病,具有复杂多样的影像学表现和预后。高分辨率计算机断层扫描(HRCT)是ILD 评估的当前标准护理成像工具。然而,HRCT 的视觉评估受到观察者间变异性和对细微变化的敏感性差的限制。这些挑战导致最近对客观和可重复的方法研究ILD 产生了巨大的兴趣。计算机辅助 CT 分析包括纹理分析和机器学习方法,最近已被证明是通过改善ILD 的特征描述和量化,对传统视觉评估的可行补充。这些定量工具不仅与肺功能测试和患者预后密切相关,而且在疾病诊断、监测和管理方面也很有用。在这篇综述中,我们概述了最近在纤维化 ILD 的诊断、预后和纵向评估方面的计算机辅助工具,同时概述了一些阻碍这些工具进一步发展的陷阱和挑战以及潜在的解决方案和进一步的努力。