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From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.
Eur Radiol. 2020 Dec; 30(12):6828-6837.ER

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).

Authors+Show Affiliations

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. Hunan Key Laboratory for Image Measurement and Vision Navigation, Changsha, Hunan, China.Department of Radiology, The First Hospital of Changsha City, Changsha, China.College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. Hunan Key Laboratory for Image Measurement and Vision Navigation, Changsha, Hunan, China.GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, 6200, MD, Maastricht, The Netherlands. Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.PingAn Technology, Shenzhen, China.PAII Inc., Bethesda, MD, USA.Department of Electrical Engineering, Eindhoven University of Technology, 5600, MB, Eindhoven, The Netherlands.Department of Radiotherapy, The First Affiliated Hospital, Zhejiang University, Zhejiang, Hangzhou, China.Hunan LanXi Biotechnology Ltd., Changsha, China.Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya Medical School, Central South University, Changsha, China.PingAn Technology, Shenzhen, China.PingAn Technology, Shenzhen, China. huanglingyun691@pingan.com.cn.Department of Respiratory Medicine, The First Hospital of Changsha City, Changsha, China. tyl71523@qq.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32683550

Citation

Li, Zhang, et al. "From Community-acquired Pneumonia to COVID-19: a Deep Learning-based Method for Quantitative Analysis of COVID-19 On Thick-section CT Scans." European Radiology, vol. 30, no. 12, 2020, pp. 6828-6837.
Li Z, Zhong Z, Li Y, et al. From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans. Eur Radiol. 2020;30(12):6828-6837.
Li, Z., Zhong, Z., Li, Y., Zhang, T., Gao, L., Jin, D., Sun, Y., Ye, X., Yu, L., Hu, Z., Xiao, J., Huang, L., & Tang, Y. (2020). From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans. European Radiology, 30(12), 6828-6837. https://doi.org/10.1007/s00330-020-07042-x
Li Z, et al. From Community-acquired Pneumonia to COVID-19: a Deep Learning-based Method for Quantitative Analysis of COVID-19 On Thick-section CT Scans. Eur Radiol. 2020;30(12):6828-6837. PubMed PMID: 32683550.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans. AU - Li,Zhang, AU - Zhong,Zheng, AU - Li,Yang, AU - Zhang,Tianyu, AU - Gao,Liangxin, AU - Jin,Dakai, AU - Sun,Yue, AU - Ye,Xianghua, AU - Yu,Li, AU - Hu,Zheyu, AU - Xiao,Jing, AU - Huang,Lingyun, AU - Tang,Yuling, Y1 - 2020/07/18/ PY - 2020/05/28/received PY - 2020/06/18/accepted PY - 2020/7/20/pubmed PY - 2020/11/11/medline PY - 2020/7/20/entrez KW - Artificial intelligence KW - COVID-19 KW - Deep learning KW - Disease progression SP - 6828 EP - 6837 JF - European radiology JO - Eur Radiol VL - 30 IS - 12 N2 - 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). SN - 1432-1084 UR - https://www.unboundmedicine.com/medline/citation/32683550/From_community_acquired_pneumonia_to_COVID_19:_a_deep_learning_based_method_for_quantitative_analysis_of_COVID_19_on_thick_section_CT_scans_ L2 - https://dx.doi.org/10.1007/s00330-020-07042-x DB - PRIME DP - Unbound Medicine ER -