Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan.
Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
J Med Radiat Sci. 2023 Mar;70(1):13-20. doi: 10.1002/jmrs.631. Epub 2022 Nov 5.
Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images.
In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance.
The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90).
We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.
为了利用影像学研究来描述患者的当前状况，从而对 2019 年冠状病毒病（COVID-19）肺炎进行检测和鉴别诊断，已经开发出了计算机辅助诊断系统。在这项放射组学研究中，我们提出了一种使用便携式胸部 X 射线图像预测 COVID-19 死亡风险患者的系统。
在这项回顾性研究中，我们从癌症影像档案（Cancer Imaging Archive）的 COVID-19-AR 数据库中选择了 100 名患者，包括 10 名死亡患者和 90 名康复患者。由于 COVID-19 患者的便携式胸部 X 射线图像分析可能较为困难，因为骨成分与该疾病的异常模式重叠，所以我们在预处理过程中采用了骨抑制技术。在左、右肺区域共测量了 620 个放射组学特征，并使用最小绝对值收缩和选择算子技术选择了 4 个放射组学特征。我们使用线性判别分析（LDA）和支持向量机（SVM）来区分死亡和康复病例。使用留一法（leave-one-out）来训练和测试分类器，并使用受试者工作特征曲线下的面积（area under the receiver-operating characteristic curve，AUC）来评估判别性能。
LDA 和 SVM 的 AUC 分别为 0.756 和 0.959。采用骨抑制技术可提高判别性能。当使用 SVM 时，预测疾病严重程度的敏感度为 90.9%（9/10），特异性为 95.6%（86/90）。
我们认为便携式胸部 X 射线图像的放射组学特征可以预测 COVID-19 死亡风险患者。
