Luo Wenting, Ren Yifei, Liu Yinuo, Deng Jun, Huang Xiaoning
The Second Clinical Medical College, Nanchang University, Nanchang, China.
Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):6123-6146. doi: 10.21037/qims-24-674. Epub 2024 Jul 18.
The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area.
We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures.
We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs.
A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.
肺癌的发病率在全球范围内呈上升趋势，在全球男性和女性中，肺癌的死亡率也最高。由于成像技术的进步以及个人进行筛查的意愿不断增加，磨玻璃结节（GGN）的检出率迅速飙升。目前，用于数据分析与解读、图像处理、疾病诊断及病变预测的人工智能（AI）方法为GGN的诊断提供了新视角。本文旨在探讨如何通过利用成像数据识别良性和恶性病变，尽早检测出恶性病变并改善临床诊断与治疗决策。本文还旨在描述计算机断层扫描（CT）引导下活检的应用，并突出该领域AI技术的发展情况。
我们使用PubMed、爱思唯尔ScienceDirect、施普林格数据库和谷歌学术搜索与本文主题相关的信息。我们收集、检查并解读了南昌大学第二附属医院影像中心的相关成像资源。此外，我们使用Adobe Illustrator 2020处理所有图表。
我们研究了GGN的常见征象，阐明了这些征象与良性和恶性病变识别之间的关系，然后描述了过去三年AI在GGN图像分割、自动分类及侵袭性预测方面的应用，包括其局限性和前景。我们还讨论了对持续存在的纯GGN进行活检的必要性。
多种成像特征可结合使用以提高GGN良恶性的诊断。应谨慎考虑使用CT引导下穿刺活检来明确病变性质。新型AI工具的开发为提高影像科医生分析GGN图像的能力及实现准确诊断带来了新的可能性和希望。
