@article{oai:kyutech.repo.nii.ac.jp:00007862,
 author = {Näppi,  Janne J. and Uemura,  Tomoki and Watari,  Chinatsu and Hironaka,  Toru and Kamiya, Tohru and 神谷, 亨 and Yoshida,  Hiroyuki},
 journal = {Scientific Reports},
 month = {Apr},
 note = {The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.},
 pages = {9263-1--9263-11},
 title = {U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19},
 volume = {11},
 year = {2021},
 yomi = {カミヤ, トオル}
}