Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT.
Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT.
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DOI：
10.1016/j.media.2021.102159
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发表时间：
2021-10
影响因子：
10.9
通讯作者：
Yoshida H
中科院分区：
文献类型：
--
作者：
Uemura T;Näppi JJ;Watari C;Hironaka T;Kamiya T;Yoshida H
由于2019冠状病毒病（COVID-19）传播迅速且临床表现广泛，快速准确地估计疾病进展和死亡率对患者管理至关重要。目前可用的COVID-19患者的基于图像的预后预测因子在很大程度上限于具有手动设计特征和监督学习的半自动化方案，并且生存分析在很大程度上限于逻辑回归。我们开发了一个弱无监督的条件生成对抗网络，称为pix 2surv，它可以被训练来直接从患者的胸部计算机断层扫描（CT）图像中估计生存分析的时间到事件信息。我们发现，在估计COVID-19患者的疾病进展和死亡率方面，基于CT图像的pix 2surv的性能显著优于现有的实验室检查和基于图像的视觉和定量预测器。因此，pix 2surv是一种很有前途的方法，用于执行基于图像的预后预测。
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.
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DOI：
10.1148/ryct.2020200075
发表时间：
2020-04-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Huang, Lu;Han, Rui;Xia, Liming
通讯作者：
Xia, Liming
影响因子：
5.9
作者：
Lanza, Ezio;Muglia, Riccardo;Balzarini, Luca
通讯作者：
Balzarini, Luca
影响因子：
11.8
作者：
Ji, Dong;Zhang, Dawei;Qin, Enqiang
通讯作者：
Qin, Enqiang
影响因子：
5.9
作者：
Francone, Marco;Iafrate, Franco;Catalano, Carlo
通讯作者：
Catalano, Carlo
DOI：
10.1148/ryct.2020200322
发表时间：
2020-08-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Homayounieh, Fatemeh;Ebrahimian, Shadi;Kalra, Mannudeep K.
通讯作者：
Kalra, Mannudeep K.