Blood Res 2022; 57(3):
Published online September 30, 2022
https://doi.org/10.5045/br.2022.2022085
© The Korean Society of Hematology
Correspondence to : Adel El-Badrawy, M.D.
Department of Radiology, Faculty of Medicine, Mansoura University, 1 Omar Ben Abdel-Aziz from Gehan Street, Mansoura 35516, Egypt
E-mail: adelelbadrawy@hotmail.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Data on the association between coronavirus disease 2019 (COVID-19) and the epidemiology and outcomes of hematological malignancies are limited. Hence, the present study aimed to assess the imaging findings using chest multidetector computed tomography (MDCT) in patients with hematologic malignancies who developed COVID-19 pneumonia.
Methods
This retrospective study included two groups, the first group consisted of COVID-19 infected patients with hematologic malignancies (100 patients), while the second group consisted of COVID-19 infected patients without hematologic malignancies or other comorbidities (100 patients). The hematological malignancies included in this study were non-Hodgkin’s lymphoma (40 patients), acute myeloid leukemia (25 patients), chronic lymphocytic leukemia (15 patients), multiple myeloma (10 patients), Hodgkin’s lymphoma (8 patients), and myelodysplastic syndrome (2 patients). Chest multidetector CT imaging was performed in all patients to assess for ground-glass opacity, consolidation, pleural effusion, and airway abnormalities.
Results
More than one CT finding was reported in each patient. No significant difference was observed in the ground-glass opacities (P=0.0594), nodule formation (P=0.2278), or airway thickening/dilatation (P=0.0566) between the two groups; meanwhile, a significant difference was observed in the degree of consolidation, the number of lobes affected, and pleural effusion (P=0.0001) as well as in the total lung severity (P=0.0001); minimal, mild, and severe affection rates; and (P=0.0047) moderate affection rates.
Conclusion
Early and reliable diagnosis of lung disease in COVID-19-infected patients may be achieved through multidetector CT imaging. Patients with hematological malignancies are more likely to have severe COVID-19 pneumonia, and radiologists should recognize the CT characteristics of this infection.
Keywords Multi-detector computed tomography, COVID-19, Pneumonia, Hematologic malignancies
Blood Res 2022; 57(3): 216-222
Published online September 30, 2022 https://doi.org/10.5045/br.2022.2022085
Copyright © The Korean Society of Hematology.
Adel El-Badrawy1, Nada Elbadrawy2
1Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura, 2Faculty of Oral and Dental Medicine, Delta University for Science and Technology, Gamasa, Dakahlya, Egypt
Correspondence to:Adel El-Badrawy, M.D.
Department of Radiology, Faculty of Medicine, Mansoura University, 1 Omar Ben Abdel-Aziz from Gehan Street, Mansoura 35516, Egypt
E-mail: adelelbadrawy@hotmail.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Data on the association between coronavirus disease 2019 (COVID-19) and the epidemiology and outcomes of hematological malignancies are limited. Hence, the present study aimed to assess the imaging findings using chest multidetector computed tomography (MDCT) in patients with hematologic malignancies who developed COVID-19 pneumonia.
Methods
This retrospective study included two groups, the first group consisted of COVID-19 infected patients with hematologic malignancies (100 patients), while the second group consisted of COVID-19 infected patients without hematologic malignancies or other comorbidities (100 patients). The hematological malignancies included in this study were non-Hodgkin’s lymphoma (40 patients), acute myeloid leukemia (25 patients), chronic lymphocytic leukemia (15 patients), multiple myeloma (10 patients), Hodgkin’s lymphoma (8 patients), and myelodysplastic syndrome (2 patients). Chest multidetector CT imaging was performed in all patients to assess for ground-glass opacity, consolidation, pleural effusion, and airway abnormalities.
Results
More than one CT finding was reported in each patient. No significant difference was observed in the ground-glass opacities (P=0.0594), nodule formation (P=0.2278), or airway thickening/dilatation (P=0.0566) between the two groups; meanwhile, a significant difference was observed in the degree of consolidation, the number of lobes affected, and pleural effusion (P=0.0001) as well as in the total lung severity (P=0.0001); minimal, mild, and severe affection rates; and (P=0.0047) moderate affection rates.
Conclusion
Early and reliable diagnosis of lung disease in COVID-19-infected patients may be achieved through multidetector CT imaging. Patients with hematological malignancies are more likely to have severe COVID-19 pneumonia, and radiologists should recognize the CT characteristics of this infection.
Keywords: Multi-detector computed tomography, COVID-19, Pneumonia, Hematologic malignancies
Table 1 . Baseline demographic findings of 100 patients with hematologic malignancies..
Hematologic malignancies | Age (yr), range (mean) | Sex | ||
---|---|---|---|---|
Male | Female | Total | ||
Non-Hodgkin lymphoma | 25–58 (40.83) | 26 | 14 | 40 |
Acute myeloid leukemia | 43–67 (48.44) | 16 | 9 | 25 |
Chronic lymphocytic leukemia | 44–75 (50.2) | 9 | 6 | 15 |
Multiple myeloma | 41–55 (47.6) | 4 | 6 | 10 |
Hodgkin lymphoma | 27–45 (33.25) | 5 | 3 | 8 |
Myelodysplastic syndrome | 61–67 (64) | 2 | - | 2 |
Total | 25–75 (44.67) | 62 | 38 | 100 |
Table 2 . Clinical presentation of COVID-19 pneumonia in 100 patients with hematologic malignancies and 100 patients from the control group..
Study group (N=100) | Control group (N=100) | ||
---|---|---|---|
Fever | 91/100 | 89/100 | 0.637 |
Cough | 83/100 | 45/100 | <0.001 |
Dyspnea | 74/100 | 29/100 | <0.001 |
Fatigue | 48/100 | 39/100 | 0.200 |
Myalgia | 67/100 | 62/100 | 0.460 |
Sore throat | 23/100 | 34/100 | 0.085 |
Chest pain | 16/100 | 14/100 | 0.692 |
Sputum production | 17/100 | 7/100 | 0.030 |
Headache | 28/100 | 26/100 | 0.750 |
Anosmia | 56/100 | 51/100 | 0.478 |
Nausea | 18/100 | 14/100 | 0.440 |
Diarrhea | 12/100 | 6/100 | 0.138 |
Abdominal pain | 14/100 | 9/100 | 0.269 |
Anorexia | 15/100 | 12/100 | 0.535 |
Blenching | 6/100 | 2/100 | 0.149 |
Table 3 . Chest MDCT findings of COVID-19 pneumonia in 100 patients with hematologic malignancies and 100 patients from the control group..
Study group (N=100) | Control group (N=100) | ||
---|---|---|---|
Ground-glass opacity | 100/100 | 95/100 | 0.0594 |
Consolidation | 80/100 | 27/100 | 0.0001 |
Nodule | 18/100 | 11/100 | 0.2278 |
N of lobes | 450/500 | 395/500 | 0.0001 |
Interlobar septal thickening | 32/100 | 24/100 | 0.2702 |
Pleural effusion | 50/100 | 7/100 | 0.0001 |
Lymphadenopathy | 16/100 | 5/100 | 0.0192 |
Airway thickening/dilatation | 70/100 | 56/100 | 0.0566 |
Central and/or peripheral | |||
Central | 10/100 | 5/100 | 0.2828 |
Peripheral | 30/100 | 58/100 | 0.0001 |
Central and peripheral | 60/100 | 37/100 | 0.0018 |
Total lung severity | |||
Minimal | 3/100 | 30/100 | 0.0001 |
Mild | 7/100 | 30/100 | 0.0001 |
Moderate | 45/100 | 25/100 | 0.0047 |
Severe | 45/100 | 15/100 | 0.0001 |
Table 4 . Incidence of lobar involvement of COVID-19 pneumonia in 100 patients with hematologic malignancies and 100 patients from the control group..
N of patients | Total N of lobes involved | ||||||
---|---|---|---|---|---|---|---|
Study group | Control group | Study group | Control group | ||||
1 lobe | 0 (0%) | 2 (2%) | 0.498 | 0 (0.0%) | 2 (0.5%) | 0.4364 | |
2 lobes | 1 (1%) | 3 (3%) | 0.621 | 2 (0.4%) | 6 (1.5%) | 0.2096 | |
3 lobes | 7 (7%) | 22 (22%) | 0.003 | 21 (4.7%) | 66 (16.7%) | <0.001 | |
4 lobes | 33 (33%) | 44 (44%) | 0.110 | 132 (29.3%) | 176 (44.6%) | <0.001 | |
5 lobes | 59 (59%) | 29 (29%) | <0.001 | 295 (65.6%) | 145 (36.7%) | <0.001 |
a)
Sang Mee Hwang
Blood Res 2024; 59():Alexander T. Phan, Ari A. Ucar, Aldin Malkoc, Janie Hu, Luke Buxton, Alan W. Tseng, Fanglong Dong, Julie P.T. Nguyễn, Arnav P. Modi, Ojas Deshpande, Johnson Lay, Andrew Ku, Dotun Ogunyemi, Sarkis Arabian
Blood Res 2023; 58(3): 138-144Mahnaz Yadollahi, Hessam Hosseinalipour, Mehrdad Karajizadeh, Muhammad Alinaqi, Pooria Fazeli, Mehrdad Jowkar, Kazem Jamali, Maryam Yadollahi
Blood Res 2023; 58(3): 127-132