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01-12-2020 | Computed Tomography | Chest

From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

Authors: Zhang Li, Zheng Zhong, Yang Li, Tianyu Zhang, Liangxin Gao, Dakai Jin, Yue Sun, Xianghua Ye, Li Yu, Zheyu Hu, Jing Xiao, Lingyun Huang, Yuling Tang

Published in: European Radiology | Issue 12/2020

Abstract

Objective

To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images.

Methods

In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen’s kappa, respectively.

Results

The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes.

Conclusions

A deep learning–based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions.

Key Points

• A deep learning–based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74).

• The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97).

• The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen’s kappa 0.8220).

Available only for authorised users

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Title: From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans
Authors: Zhang Li
Zheng Zhong
Yang Li
Tianyu Zhang
Liangxin Gao
Dakai Jin
Yue Sun
Xianghua Ye
Li Yu
Zheyu Hu
Jing Xiao
Lingyun Huang
Yuling Tang
Publication date: 01-12-2020
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Community-Acquired Pneumonia
COVID-19
Biomarkers
Published in: European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07042-x

At a glance: The STEP trials

Springer Medicine

From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

Abstract

Objective

Methods

Results

Conclusions

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Key Points

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