Ruichao Niu1, Shuming Ye2, Yongfeng Li3, Hua Ma4, Xiaoting Xie5, Shilian Hu6, Xiaoming Huang7, Yangshu Ou1, Jie Chen1. 1. Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, PR China. 2. Department of Respiratory Medicine, Wuhan First Hospital/Wuhan Hospital of Traditional Chinese and Western Medicine, Wuhan, PR China. 3. Department of Respiratory Medicine, Anyang District Hospital, Anyang, PR China. 4. Department of Infectious Disease, People's Hospital of Liuyang City, Liuyang, PR China. 5. Department of Respiratory Medicine, People's Hospital of Ningxiang City, Ningxiang, PR China. 6. Department of Radiology and Imaging, The Third Hospital of Yongzhou City, Yongzhou, PR China. 7. Department of Radiology and Imaging, Traditional Chinese Medicine Hospital of Leiyang City, Hengyang, PR China.
Abstract
OBJECTIVES: Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19 patients. METHODS: Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled. RESULTS: This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01). CONCLUSION: Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.
OBJECTIVES:Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19patients. METHODS:Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled. RESULTS: This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01). CONCLUSION: Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.
Authors: Jasjit S Suri; Sushant Agarwal; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro S C Danna; Luca Saba; Armin Mehmedović; Gavino Faa; Inder M Singh; Monika Turk; Paramjit S Chadha; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanasios D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Pudukode R Krishnan; Ferenc Nagy; Zoltan Ruzsa; Mostafa M Fouda; Subbaram Naidu; Klaudija Viskovic; Mannudeep K Kalra Journal: Diagnostics (Basel) Date: 2022-06-16
Authors: Mohit Agarwal; Sushant Agarwal; Luca Saba; Gian Luca Chabert; Suneet Gupta; Alessandro Carriero; Alessio Pasche; Pietro Danna; Armin Mehmedovic; Gavino Faa; Saurabh Shrivastava; Kanishka Jain; Harsh Jain; Tanay Jujaray; Inder M Singh; Monika Turk; Paramjit S Chadha; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; David W Sobel; Martin Miner; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Pudukode R Krishnan; Rajanikant R Yadav; Frence Nagy; Zsigmond Tamás Kincses; Zoltan Ruzsa; Subbaram Naidu; Klaudija Viskovic; Manudeep K Kalra; Jasjit S Suri Journal: Comput Biol Med Date: 2022-05-21 Impact factor: 6.698
Authors: Jasjit S Suri; Sushant Agarwal; Luca Saba; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro Danna; Armin Mehmedović; Gavino Faa; Tanay Jujaray; Inder M Singh; Narendra N Khanna; John R Laird; Petros P Sfikakis; Vikas Agarwal; Jagjit S Teji; Rajanikant R Yadav; Ferenc Nagy; Zsigmond Tamás Kincses; Zoltan Ruzsa; Klaudija Viskovic; Mannudeep K Kalra Journal: J Med Syst Date: 2022-08-21 Impact factor: 4.920