School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
Department of Molecular Imaging, Royal Prince Alfred Hospital, Camperdown, NSW, 2050, Australia.
J Digit Imaging. 2023 Dec;36(6):2356-2366. doi: 10.1007/s10278-023-00895-w. Epub 2023 Aug 8.
Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled 'radiogenomics', a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research.
新型冠状病毒病 2019（COVID-19）是由严重急性呼吸系统综合征冠状病毒 2 引起的，该病毒通过血管紧张素转换酶 2（ACE2）进入人体并改变其基因表达。改变的 ACE2 在 COVID-19 的发病机制中起着至关重要的作用。然而，基因表达谱分析具有侵袭性和昂贵性，并且不常规进行。相比之下，医学成像（如计算机断层扫描（CT））可以捕获描绘异常的成像特征，并且广泛可用。图像特征的计算机化定量已经实现了“放射组学”，这是一门识别与分子特征相关的图像特征的研究学科。由于 COVID-19 患者缺乏 ACE2 表达数据，因此 ACE2 与 COVID-19 之间的放射组学尚未进行。与 COVID-19 相似，肺腺癌（LUAD）患者表现出改变的 ACE2 表达，并且 LUAD 数据丰富。我们提出了一种放射组学框架，用于从 LUAD 中提取与 ACE2 表达数据相关的图像特征（ACE2-RGF）。然后，将 ACE2-RGF 用作 ACE2 表达的替代生物标志物。我们采用了传统的特征选择技术，包括 ElasticNet 和 LASSO。我们的结果表明：i）与传统技术相比，ACE2-RGF 编码了一组独特的图像特征，ii）ACE2-RGF 可以使用与传统特征选择技术相当的 AUC 为 0.92 的性能从正常受试者中分类 COVID-19，iii）ACE2-RGF 可以有效地识别患有危重病的患者，AUC 为 0.85。这些发现为自动 COVID-19 分析和未来研究提供了独特的见解。
