Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran.
Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran.
Phys Eng Sci Med. 2022 Sep;45(3):747-755. doi: 10.1007/s13246-022-01140-4. Epub 2022 Jul 7.
The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.
本研究旨在使用基于肺部计算机断层扫描图像的放射组学方法，将疑似 COVID-19 的患者分为五个阶段：正常、早期、进展、高峰和吸收阶段。评估了 683 人的肺部 CT 扫描。从每个 CT 图像中提取了一组统计纹理特征。使用随机森林算法将人群分为五组 COVID-19 疾病阶段，该算法是基于决策树输出的集成方法。与其他三种常见分类器相比，所提出的方法取得了最高的结果，准确率为 93.55%（正常组为 96.25%，早期组为 74.39%，进展组为 100%，高峰组为 82.19%，吸收组为 96%）。放射组学方法可用于 COVID-19 疾病阶段的分类，具有较高的准确性，有助于确定患者住院所需的时间，确定每个类别患者所需的治疗过程类型，并降低住院患者的护理和治疗成本。
