PDF(Pubmed)
Abstract：
This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.
摘要：
这项观察性研究调查了影像组学作为CT的非侵入性辅助手段在区分COVID-19肺结节与其他良性和恶性肺结节方面的潜力。病变分割,特征提取,和机器学习算法，包括决策树，支持向量机，随机森林,前馈神经网络，和判别分析，被用于影像组学工作流程。关键功能，如Idmn，偏斜度,长期的低灰度强调被认为是区分的关键。该模型在区分COVID-19与其他良性结节方面的准确率为83%，与恶性结节的准确率为88%。这项研究得出的结论是，放射学，通过机器学习，作为非侵入性区分COVID-19和其他良性和恶性肺结节的有价值的工具。研究结果表明，影像组学在COVID-19肺炎患者中具有潜在的补充作用，这些患者表现出肺结节并怀疑并发肺部病变。临床相关性在于利用影像组学分析进行特征提取和分类，有助于增强肺结节的分化，特别是在COVID-19的背景下。
