1
|
Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study. J Comput Assist Tomogr 2022; 46:413-422. [PMID: 35405709 DOI: 10.1097/rct.0000000000001303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images. METHODS This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115). RESULTS In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035). CONCLUSIONS Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.
Collapse
|
2
|
Soya E, Ekenel N, Savas R, Toprak T, Bewes J, Doganay O. Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia. COSMODERMA 2022; 12:6. [PMID: 35251762 PMCID: PMC8889935 DOI: 10.25259/jcis_172_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 01/28/2022] [Indexed: 11/30/2022]
Abstract
Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.
Collapse
Affiliation(s)
- Elif Soya
- Department of Basic Oncology, Institute of Health Sciences, Ege University, Izmir, Turkey,
| | - Nur Ekenel
- Department of Basic Oncology, Institute of Health Sciences, Ege University, Izmir, Turkey,
| | - Recep Savas
- Department of Radiology, Faculty of Medicine, Ege University, Izmir, Turkey,
| | - Tugce Toprak
- Department of Electrical and Electronics Engineering, Institute of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey,
| | - James Bewes
- South East Radiology, New South Wales, Australia,
| | - Ozkan Doganay
- Department of Basic Oncology, Institute of Health Sciences, Ege University, Izmir, Turkey,
| |
Collapse
|
3
|
Inui S, Yoon SH, Doganay O, Gleeson FV, Kim M. Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation. PLoS One 2022; 17:e0263158. [PMID: 35077496 PMCID: PMC8789183 DOI: 10.1371/journal.pone.0263158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/12/2022] [Indexed: 01/08/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) may severely impair pulmonary function and cause hypoxia. However, the association of COVID-19 pneumonia on CT with impaired ventilation remains unexplained. This pilot study aims to demonstrate the relationship between the radiological findings on COVID-19 CT images and ventilation abnormalities simulated in a computational model linked to the patients’ symptoms. Methods Twenty-five patients with COVID-19 and four test-negative healthy controls who underwent a baseline non-enhanced CT scan: 7 dyspneic patients, 9 symptomatic patients without dyspnea, and 9 asymptomatic patients were included. A 2D U-Net-based CT segmentation software was used to quantify radiological futures of COVID-19 pneumonia. The CT image-based full-scale airway network (FAN) flow model was employed to assess regional lung ventilation. Functional and radiological features were compared across groups and correlated with the clinical symptoms. Heterogeneity in ventilation distribution and ventilation defects associated with the pneumonia and the patients’ symptoms were assessed. Results Median percentage ventilation defects were 0.2% for healthy controls, 0.7% for asymptomatic patients, 1.2% for symptomatic patients without dyspnea, and 11.3% for dyspneic patients. The median of percentage pneumonia was 13.2% for dyspneic patients and 0% for the other groups. Ventilation defects preferentially affected the posterior lung and worsened with increasing pneumonia linearly (y = 0.91x + 0.99, R2 = 0.73) except for one of the nine dyspneic patients who had disproportionally large ventilation defects (7.8% of the entire lung) despite mild pneumonia (1.2%). The symptomatic and dyspneic patients showed significantly right-skewed ventilation distributions (symptomatic without dyspnea: 0.86 ± 0.61, dyspnea 0.91 ± 0.79) compared to the patients without symptom (0.45 ± 0.35). The ventilation defect analysis with the FAN model provided a comparable diagnostic accuracy to the percentage pneumonia in identifying dyspneic patients (area under the receiver operating characteristic curve, 0.94 versus 0.96). Conclusions COVID-19 pneumonia segmentations from CT scans are accompanied by impaired pulmonary ventilation preferentially in dyspneic patients. Ventilation analysis with CT image-based computational modelling shows it is able to assess functional impairment in COVID-19 and potentially identify one of the aetiologies of hypoxia in patients with COVID-19 pneumonia.
Collapse
Affiliation(s)
- Shohei Inui
- Departments of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Departments of Radiology, Japan Self- Defense Forces Central Hospital, Tokyo, Japan
| | - Soon Ho Yoon
- Department of Radiology, UMass Memorial Medical Center, Worcester, MA, United States of America
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Ozkan Doganay
- Healthy Science Institute, Ege University, Izmir, Turkey
| | - Fergus V. Gleeson
- Department of Oncology, University of Oxford, Oxford, United Kingdom
- Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, United Kingdom
| | - Minsuok Kim
- Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom
- * E-mail:
| |
Collapse
|
4
|
Yoon SH, Ham SY, Nam BD, Chae KJ, Lee D, Yoo JY, Bak SH, Kim JY, Kim JH, Kim KB, Jung JI, Lim JK, Lee JE, Chung MJ, Lee YK, Kim YS, Jo JE, Lee SM, Kwon W, Park CM, Kim YH, Jeong YJ. Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019. J Korean Med Sci 2020; 35:e413. [PMID: 33258333 PMCID: PMC7707929 DOI: 10.3346/jkms.2020.35.e413] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/13/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. METHODS The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. RESULTS The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11-97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. CONCLUSION The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.
Collapse
Affiliation(s)
- Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | - Soo Youn Ham
- Department of Radiology, Kangbuk Samsung Hospital, Seoul, Korea
| | - Bo Da Nam
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Dabee Lee
- Department of Radiology, Dankook University Hospital, Cheonan, Korea
| | - Jin Young Yoo
- Department of Radiology, Chungbuk National University Hospital, Cheongju, Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Korea
| | - Jin Hwan Kim
- Department of Radiology, Chungnam National University Hospital, College of Medicine, Daejeon, Korea
| | - Ki Beom Kim
- Department of Radiology, Daegu Fatima Hospital, Daegu, Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jae Kwang Lim
- Department of Radiology, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | - Young Seon Kim
- Department of Radiology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Korea
| | - Ji Eun Jo
- Department of Radiology, Busan Medical Center, Busan, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Woocheol Kwon
- Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea
| | - Yun Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Korea
| | - Yeon Joo Jeong
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Biomedical Research Institute, Busan, Korea.
| |
Collapse
|