Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnosti...
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2021
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oai:doaj.org-article:cddf71bcf46b493ba6abd98058ce246a2021-11-25T17:58:37ZSeverity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT10.3390/info121104712078-2489https://doaj.org/article/cddf71bcf46b493ba6abd98058ce246a2021-11-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/471https://doaj.org/toc/2078-2489Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.You-Zhen FengSidong LiuZhong-Yuan ChengJuan C. QuirozDana RezazadeganPing-Kang ChenQi-Ting LinLong QianXiao-Fang LiuShlomo BerkovskyEnrico CoieraLei SongXiao-Ming QiuXiang-Ran CaiMDPI AGarticlechest CTCOVID-19severity assessmentprogression predictionU-NetRNNInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 471, p 471 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
chest CT COVID-19 severity assessment progression prediction U-Net RNN Information technology T58.5-58.64 |
| spellingShingle |
chest CT COVID-19 severity assessment progression prediction U-Net RNN Information technology T58.5-58.64 You-Zhen Feng Sidong Liu Zhong-Yuan Cheng Juan C. Quiroz Dana Rezazadegan Ping-Kang Chen Qi-Ting Lin Long Qian Xiao-Fang Liu Shlomo Berkovsky Enrico Coiera Lei Song Xiao-Ming Qiu Xiang-Ran Cai Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| description |
Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. |
| format |
article |
| author |
You-Zhen Feng Sidong Liu Zhong-Yuan Cheng Juan C. Quiroz Dana Rezazadegan Ping-Kang Chen Qi-Ting Lin Long Qian Xiao-Fang Liu Shlomo Berkovsky Enrico Coiera Lei Song Xiao-Ming Qiu Xiang-Ran Cai |
| author_facet |
You-Zhen Feng Sidong Liu Zhong-Yuan Cheng Juan C. Quiroz Dana Rezazadegan Ping-Kang Chen Qi-Ting Lin Long Qian Xiao-Fang Liu Shlomo Berkovsky Enrico Coiera Lei Song Xiao-Ming Qiu Xiang-Ran Cai |
| author_sort |
You-Zhen Feng |
| title |
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| title_short |
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| title_full |
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| title_fullStr |
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| title_full_unstemmed |
Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT |
| title_sort |
severity assessment and progression prediction of covid-19 patients based on the lesionencoder framework and chest ct |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/cddf71bcf46b493ba6abd98058ce246a |
| work_keys_str_mv |
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| _version_ |
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