Unsupervised survival prediction model from CT images of patients with COVID-19

We have developed a survival prediction model, called pix2surv, based on a conditional generative adversarial network, which is capable of directly estimating the survival time from chest CT images of a patient. We evaluated its performance on the prediction of the overall survival of patients with COVID-19 in comparison with existing clinical biomarkers including the blood tests of lactic dehydrogenase, lymphocyte, and C-reactive protein. The pix2surv model yielded significantly higher performance than those of the clinical biomarkers in the prediction of the overall survival of the COVID-19 patients, indicating the high effectiveness of the pix2surv model as a prognostic imaging biomarker for patients with COVID-19.