3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.
3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Med Image Anal. 2021 Oct;73:102159. doi: 10.1016/j.media.2021.102159. Epub 2021 Jul 11.
Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions.
由于 2019 年冠状病毒病（COVID-19）的临床表现迅速传播和广泛，快速准确地估计疾病的进展和死亡率对于患者的管理至关重要。目前用于 COVID-19 患者的基于图像的预后预测因子在很大程度上仅限于具有手动设计特征和监督学习的半自动方案，生存分析在很大程度上仅限于逻辑回归。我们开发了一种弱无监督条件生成对抗网络，称为 pix2surv，它可以从患者的胸部计算机断层扫描（CT）图像中进行训练，以直接估计生存分析的时间事件信息。我们表明，基于 CT 图像的 pix2surv 的性能在估计 COVID-19 患者的疾病进展和死亡率方面明显优于现有实验室测试以及基于图像的视觉和定量预测因子。因此，pix2surv 是进行基于图像的预后预测的一种很有前途的方法。
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