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Gong K, Wu D, Arru CD, Homayounieh F, Neumark N, Guan J, Buch V, Kim K, Bizzo BC, Ren H, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Guo N, Digumarthy SR, Dayan I, Kalra MK, Li Q. A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records. Eur J Radiol 2021; 139:109583. [PMID: 33846041 PMCID: PMC7863774 DOI: 10.1016/j.ejrad.2021.109583] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 01/28/2021] [Accepted: 02/01/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction. METHOD We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction. RESULTS For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort. CONCLUSION The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.
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Affiliation(s)
- Kuang Gong
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Dufan Wu
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Chiara Daniela Arru
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Nir Neumark
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | | | - Varun Buch
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | - Kyungsang Kim
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | | | - Hui Ren
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Won Young Tak
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Soo Young Park
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Yu Rim Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Min Kyu Kang
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Jung Gil Park
- Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea
| | - Alessandro Carriero
- Radiologia, Azienda Ospedaliera Universitaria Maggiore della Carità, Novara, Italy
| | - Luca Saba
- Radiologia, Azienda Ospedaliera Universitaria Policlinico di Monserrato, Italy
| | - Mahsa Masjedi
- Department of Radiology, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamidreza Talari
- Department of Radiology, Kashan University of Medical Sciences, Kashan, Iran
| | - Rosa Babaei
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Ning Guo
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital, Boston, United States
| | - Ittai Dayan
- MGH & BWH Center for Clinical Data Science, Boston, United States
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, United States.
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, United States.
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2
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Ebrahimian S, Oliveira Bernardo M, Alberto Moscatelli A, Tapajos J, Leitão Tapajós L, Jamil Khoury H, Babaei R, Karimi Mobin H, Mohseni I, Arru C, Carriero A, Falaschi Z, Pasche A, Saba L, Homayounieh F, Bizzo BC, Vassileva J, Kalra MK. Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues. Phys Med 2021; 84:125-131. [PMID: 33894582 PMCID: PMC8058535 DOI: 10.1016/j.ejmp.2021.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/24/2021] [Accepted: 04/01/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. Methods With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). Results The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11–40% relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027–0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). Conclusions Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses.
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Affiliation(s)
- Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA
| | - Monica Oliveira Bernardo
- Hospital Miguel Soeiro - UNIMED, Pontificia University Catholic of São Paulo - PUC-SP, Sorocaba, São Paulo, Brazil
| | - Antônio Alberto Moscatelli
- Hospital Miguel Soeiro - UNIMED, Pontificia University Catholic of São Paulo - PUC-SP, Sorocaba, São Paulo, Brazil
| | - Juliana Tapajos
- Hospital Delphina Rinaldi Abdel Aziz, Manaus, Amazonas, Brazil
| | | | - Helen Jamil Khoury
- Nuclear Energy Department, Federal University of Pernambuco, Recife, Pernambuco, Brazil
| | - Rosa Babaei
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Iman Mohseni
- Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Chiara Arru
- Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy
| | | | | | | | - Luca Saba
- Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA
| | - Bernardo C Bizzo
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA
| | - Jenia Vassileva
- Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.
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3
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Homayounieh F, Bezerra Cavalcanti Rockenbach MA, Ebrahimian S, Doda Khera R, Bizzo BC, Buch V, Babaei R, Karimi Mobin H, Mohseni I, Mitschke M, Zimmermann M, Durlak F, Rauch F, Digumarthy SR, Kalra MK. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. J Digit Imaging 2021; 34:320-329. [PMID: 33634416 PMCID: PMC7906242 DOI: 10.1007/s10278-021-00430-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/08/2021] [Accepted: 02/02/2021] [Indexed: 12/14/2022] Open
Abstract
To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
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Affiliation(s)
- Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
| | | | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
| | - Ruhani Doda Khera
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
| | - Bernardo C. Bizzo
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
- MGH & BWH Center for Clinical Data Science, Boston, MA USA
| | - Varun Buch
- MGH & BWH Center for Clinical Data Science, Boston, MA USA
| | - Rosa Babaei
- Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran
| | - Iman Mohseni
- Department of Radiology, Firoozgar Hospital and Iran University of Medical Sciences, Tehran, Iran
| | | | | | - Felix Durlak
- Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany
| | - Franziska Rauch
- Diagnostic Imaging, Siemens Healthcare GmbH, Erlangen, Germany
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA USA
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4
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Javaheri T, Homayounfar M, Amoozgar Z, Reiazi R, Homayounieh F, Abbas E, Laali A, Radmard AR, Gharib MH, Mousavi SAJ, Ghaemi O, Babaei R, Mobin HK, Hosseinzadeh M, Jahanban-Esfahlan R, Seidi K, Kalra MK, Zhang G, Chitkushev LT, Haibe-Kains B, Malekzadeh R, Rawassizadeh R. CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images. NPJ Digit Med 2021; 4:29. [PMID: 33603193 PMCID: PMC7893172 DOI: 10.1038/s41746-021-00399-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/10/2020] [Indexed: 12/21/2022] Open
Abstract
Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.
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Affiliation(s)
- Tahereh Javaheri
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
| | - Morteza Homayounfar
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Zohreh Amoozgar
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Reza Reiazi
- Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Medical Physics, School of Medicine, Iran university of Medical Sciences, Tehran, Iran
| | - Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Engy Abbas
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Azadeh Laali
- Department of Infectious Diseases, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Gharib
- Department of Radiology and Golestan Rheumatology Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | | | - Omid Ghaemi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Rosa Babaei
- Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Rana Jahanban-Esfahlan
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Khaled Seidi
- Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Guanglan Zhang
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA
| | - L T Chitkushev
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Reza Malekzadeh
- Digestive Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Rawassizadeh
- Health Informatics Lab, Metropolitan College, Boston University, Boston, USA.
- Department of Computer Science, Metropolitan College, Boston University, Boston, USA.
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5
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Wu D, Gong K, Arru CD, Homayounieh F, Bizzo B, Buch V, Ren H, Kim K, Neumark N, Xu P, Liu Z, Fang W, Xie N, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Carriero A, Saba L, Masjedi M, Talari H, Babaei R, Mobin HK, Ebrahimian S, Dayan I, Kalra MK, Li Q. Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels. IEEE J Biomed Health Inform 2020; 24:3529-3538. [PMID: 33044938 PMCID: PMC8545170 DOI: 10.1109/jbhi.2020.3030224] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 08/19/2020] [Accepted: 09/26/2020] [Indexed: 11/09/2022]
Abstract
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.
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Affiliation(s)
- Dufan Wu
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Kuang Gong
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | | | | | - Bernardo Bizzo
- MGH & BWH Center for Clinical Data ScienceBostonMA02114USA
| | - Varun Buch
- MGH & BWH Center for Clinical Data ScienceBostonMA02114USA
| | - Hui Ren
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Kyungsang Kim
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Nir Neumark
- MGH & BWH Center for Clinical Data ScienceBostonMA02114USA
| | - Pengcheng Xu
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Zhiyuan Liu
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Wei Fang
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Nuobei Xie
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Won Young Tak
- Department of Internal Medicine, School of MedicineKyungpook National UniversityDaegu41944South Korea
| | - Soo Young Park
- Department of Internal Medicine, School of MedicineKyungpook National UniversityDaegu41944South Korea
| | - Yu Rim Lee
- Department of Internal Medicine, School of MedicineKyungpook National UniversityDaegu41944South Korea
| | - Min Kyu Kang
- Department of Internal MedicineYeungnam University College of MedicineDaegu41944South Korea
| | - Jung Gil Park
- Department of Internal MedicineYeungnam University College of MedicineDaegu41944South Korea
| | - Alessandro Carriero
- RadiologiaAzienda Ospedaliera Universitaria Maggiore della Carità28100NovaraItaly
| | - Luca Saba
- RadiologiaAzienda Ospedaliera Universitaria Policlinico di Cagliari09124CagliariItaly
| | - Mahsa Masjedi
- Department of RadiologyShahid Beheshti HospitalKashan00000Iran
| | | | - Rosa Babaei
- Department of Radiology, Firoozgar HospitalIran University of Medical SciencesTehran48711-15937Iran
| | - Hadi Karimi Mobin
- Department of Radiology, Firoozgar HospitalIran University of Medical SciencesTehran48711-15937Iran
| | - Shadi Ebrahimian
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
| | - Ittai Dayan
- MGH & BWH Center for Clinical Data ScienceBostonMA02114USA
| | | | - Quanzheng Li
- Department of RadiologyMassachusetts General HospitalBostonMA02114USA
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6
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Homayounieh F, Zhang EW, Babaei R, Karimi Mobin H, Sharifian M, Mohseni I, Kuo A, Arru C, Kalra MK, Digumarthy SR. Clinical and imaging features predict mortality in COVID-19 infection in Iran. PLoS One 2020; 15:e0239519. [PMID: 32970733 PMCID: PMC7514030 DOI: 10.1371/journal.pone.0239519] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 09/08/2020] [Indexed: 01/10/2023] Open
Abstract
The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.
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Affiliation(s)
- Fatemeh Homayounieh
- Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Eric W Zhang
- Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Rosa Babaei
- Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran
| | - Hadi Karimi Mobin
- Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran
| | - Maedeh Sharifian
- Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran
| | - Iman Mohseni
- Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran
| | - Anderson Kuo
- Division of Cardiovascular Imaging, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Chiara Arru
- Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Mannudeep K Kalra
- Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Subba R Digumarthy
- Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America
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7
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Homayounieh F, Ebrahimian S, Babaei R, Mobin HK, Zhang E, Bizzo BC, Mohseni I, Digumarthy SR, Kalra MK. CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia. Radiol Cardiothorac Imaging 2020; 2:e200322. [PMID: 33778612 PMCID: PMC7380121 DOI: 10.1148/ryct.2020200322] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/29/2020] [Accepted: 07/10/2020] [Indexed: 01/08/2023]
Abstract
Purpose To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables. Materials and Methods This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed. Results Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission. Conclusion Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.
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Affiliation(s)
- Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Rosa Babaei
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Hadi Karimi Mobin
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Eric Zhang
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Bernardo Canedo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Iman Mohseni
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
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