U-radiomics for predicting survival of patients with COVID-19

Tomoki Uemura; Janne Näppi; Chinatsu Watari; Tohru Kamiya; Hiroyuki Yoshida

doi:10.1117/12.2581907

15 March 2021 U-radiomics for predicting survival of patients with COVID-19

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 1160110 (2021) https://doi.org/10.1117/12.2581907
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

We developed and evaluated the effect of deep radiomic features, called U-radiomics, on the prediction of the overall survival of patients with the coronavirus disease 2019 (COVID-19). A U-net was trained on chest CT images of patients with interstitial lung diseases to classify lung regions of interest into five characteristic lung tissue patterns. The trained Unet was applied to the chest CT images of patients with COVID-19, and a U-radiomics vector for each patient was identified from the bottleneck layer of the U-net across all the axial CT images of the patient. The U-radiomics vector was subjected to a Cox proportional hazards model with an elastic-net penalty for predicting the survival of the patient. The evaluation was performed by use of bootstrapping, where the concordance index (C-index) was used as the comparative performance metric. Our preliminary comparative evaluation of existing prognostic biomarkers and the proposed U-survival model yielded the C-index values of (a) extent of well-aerated lung parenchyma: 51%, (b) combination of blood tests of lactic dehydrogenase, lymphocyte, and C-reactive protein: 63%, and (c) U-survival: 87%. Thus, the U-survival significantly outperformed clinical biomarkers in predicting the survival of COVID-19 patients, indicating that the U-radiomics vector of the U-survival model may provide a highly accurate prognostic biomarker for patients with COVID-19.

Conference Presentation

Citation Download Citation

Tomoki Uemura, Janne Näppi, Chinatsu Watari, Tohru Kamiya, and Hiroyuki Yoshida "U-radiomics for predicting survival of patients with COVID-19", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 1160110 (15 March 2021); https://doi.org/10.1117/12.2581907

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available