Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study

Author(s): Armando Ugo Cavallo*, Jacopo Troisi, Marco Forcina, Pier-Valerio Mari, Valerio Forte, Massimiliano Sperandio, Sergio Pagano, Pierpaolo Cavallo, Roberto Floris and Francesco Garaci

Volume 17, Issue 9, 2021

Published on: 12 January, 2021

Page: [1094 - 1102] Pages: 9

DOI: 10.2174/1573405617999210112195450

Abstract

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia.

Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images.

Methods: Chest X-ray images were accessed from a publicly available repository(https://www.kaggle. com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal region of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis.

Results: Six models, namely NB, GLM, DL, GBT, ANN, and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest area under the curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated by evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity.

Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.

Keywords: X-ray, COVID-19, pneumonia, thorax, interstitial pneumonia, radiomics, texture analysis.

Graphical Abstract

[1]
Chen L, Liu W, Zhang Q, et al. RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect 2020; 9(1): 313-9.
[http://dx.doi.org/10.1080/22221751.2020.1725399] [PMID: 32020836]
[2]
Zhu N, Zhang D, Wang W, et al. China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382(8): 727-33.
[http://dx.doi.org/10.1056/NEJMoa2001017] [PMID: 31978945]
[3]
Mahase E. Covid-19: WHO declares pandemic because of “alarming levels” of spread, severity, and inaction. BMJ 2020; 368(m1036): m1036.
[http://dx.doi.org/10.1136/bmj.m1036] [PMID: 32165426]
[4]
Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 2020; 323(11): 1061-9.
[http://dx.doi.org/10.1001/jama.2020.1585] [PMID: 32031570]
[5]
Wölfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature 2020; 581(7809): 465-9.
[http://dx.doi.org/10.1038/s41586-020-2196-x] [PMID: 32235945]
[6]
Wang W, Xu Y, Gao R, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 2020; 323(18): 1843-4.
[http://dx.doi.org/10.1001/jama.2020.3786] [PMID: 32159775]
[7]
Sethuraman N, Jeremiah SS, Ryo A. Interpreting diagnostic tests for SARS-CoV-2. JAMA 2020; 323(22): 2249-51.
[http://dx.doi.org/10.1001/jama.2020.8259] [PMID: 32374370]
[8]
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A teport of 1014 cases. Radiology 2020; 296(2): E32-40.
[9]
Wong HYF, Lam HYS, Fong AH-T, Leung ST, Chin TW-Y, Lo CSY. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology 2020; 296(2): E72-8.
[PMID: 32216717]
[10]
Nasir MU, Roberts J, Muller NL, et al. The role of emergency radiology in COVID-19: From preparedness to diagnosis [Formula: see text]. Can Assoc Radiol J 2020; 71(3): 293-300.
[http://dx.doi.org/10.1177/0846537120916419] [PMID: 32233876]
[11]
Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J. Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology 2020; 296(2): E41-5.
[12]
Li Y, Xia L. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. AJR Am J Roentgenol 2020; 214(6): 1280-6.
[http://dx.doi.org/10.2214/AJR.20.22954] [PMID: 32130038]
[13]
Jalaber C, Lapotre T, Morcet-Delattre T, Ribet F, Jouneau S, Lederlin M. Chest CT in COVID-19 pneumonia: a review of current knowledge. Diagnos Intern Imaging 2020; 101(7): 431-7.
[14]
Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 2020; 20(4): 425-34.
[15]
Hosseinpour-Niazi S, Bakhshi B, Betru E, Mirmiran P, Darand M, Azizi F. Prospective study of total and various types of vegetables and the risk of metabolic syndrome among children and adolescents. World J Diabetes 2019; 10(6): 362-75.
[http://dx.doi.org/10.4239/wjd.v10.i6.362] [PMID: 31231459]
[16]
Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 2020; 295(3): 200463.
[17]
Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet 2020; 395(10231): 1225-8.
[http://dx.doi.org/10.1016/S0140-6736(20)30627-9] [PMID: 32178769]
[18]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278(2): 563-77.
[http://dx.doi.org/10.1148/radiol.2015151169] [PMID: 26579733]
[19]
Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture analysis of imaging: what radiologists need to know. AJR Am J Roentgenol 2019; 212(3): 520-8.
[http://dx.doi.org/10.2214/AJR.18.20624] [PMID: 30645163]
[20]
Giambelluca D, Cannella R, Vernuccio F, et al. PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer. Curr Probl Diagn Radiol 2019; 50(2): 175-85.
[http://dx.doi.org/10.1067/j.cpradiol.2019.10.009] [PMID: 31761413]
[21]
Kolossváry M, Kellermayer M, Merkely B, Maurovich-Horvat P. Cardiac computed tomography radiomics. J Thorac Imaging 2018; 33(1): 26-34.
[http://dx.doi.org/10.1097/RTI.0000000000000268] [PMID: 28346329]
[22]
Fang M, He B, Li L, Dong D, Yang X, Li C. CT radiomics can help screen the coronavirus disease 2019 (COVID-19): A preliminary study. Sci China Inf Sci 2020; 63(7): 172103.
[http://dx.doi.org/10.1007/s11432-020-2849-3]
[23]
Belfiore MP, Urraro F, Grassi R, et al. Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med (Torino) 2020; 125(5): 500-4.
[http://dx.doi.org/10.1007/s11547-020-01195-x] [PMID: 32367319]
[24]
Murphy K, Smits H, Knoops AJ, et al. Covid-19 on the chest radiograph: a multi-reader evaluation of an Ai system. Radiology 2020; 296(3): E167-72.
[25]
Oh Y, Park S, Ye JC. Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans Med Imaging 2020; 39(8): 2688-700.
[http://dx.doi.org/10.1109/TMI.2020.2993291] [PMID: 32396075]
[26]
Chowdhury DrM, Rahman T, Khandakar A, et al. Can AI help in screening Viral and COVID-19 pneumonia? IEEE Access 2020; 8: 132665-76.
[27]
Szczypiński PM, Strzelecki M, Materka A, Klepaczko A. MaZda-A software package for image texture analysis. Comput Meth Programs Biomed 2009; 94(1): 66-76.
[28]
Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 2004; 22(1): 81-91.
[http://dx.doi.org/10.1016/j.mri.2003.09.001]
[29]
Troisi J, Sarno L, Martinelli P, et al. A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies. Metabolomics 2017; 13(11): 140.
[http://dx.doi.org/10.1007/s11306-017-1274-z]
[30]
Troisi J, Landolfi A, Sarno L, et al. A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies. Metabolomics 2018; 14(6): 77.
[http://dx.doi.org/10.1007/s11306-018-1370-8] [PMID: 30830338]
[31]
Troisi J, Sarno L, Landolfi A, et al. Metabolomic signature of endometrial cancer. J Proteome Res 2018; 17(2): 804-12.
[http://dx.doi.org/10.1021/acs.jproteome.7b00503] [PMID: 29235868]
[32]
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988; 44(3): 837-45.
[http://dx.doi.org/10.2307/2531595] [PMID: 3203132]
[33]
Development CORE TEAM R.. A language and environment for statistical computing. R foundation for statistical computing Vienna, Austria. 2008.
[34]
Fan L, Liu S. CT and COVID-19: Chinese experience and recommendations concerning detection, staging and follow-up. Eur Radiol 2020; 30(9): 5214-6.
[http://dx.doi.org/10.1007/s00330-020-06898-3] [PMID: 32377812]
[35]
Soldati G, Smargiassi A, Inchingolo R, et al. Is there a role for lung ultrasound during the COVID-19 pandemic? J Ultrasound Med 2020; 39(7): 1459-62.
[http://dx.doi.org/10.1002/jum.15284] [PMID: 32198775]
[36]
Soldati G, Smargiassi A, Inchingolo R, et al. Proposal for international standardization of the use of lung ultrasound for patients with COVID-19: A simple, quantitative, reproducible method. J Ultrasound Med 2020; 39(7): 1413-9.
[http://dx.doi.org/10.1002/jum.15285] [PMID: 32227492]
[37]
Bai Y, Yao L, Wei T, Tian F, Jin D-Y, Chen L. Presumed asymptomatic carrier transmission of COVID-19. JAMA 2020; 323(14): 1406-7.
[38]
Sajadi M, Habibzadeh P, Vintzileos A, Shokouhi S, Miralles-Wilhelm F, Amoroso A. Temperature and latitude analysis to predict potential spread and seasonality for COVID-19. SSRN Elect J 2020. Version 1. Preprint.
[39]
Ackermann M, Verleden SE, Kuehnel M, et al. Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in Covid-19. N Engl J Med 2020; 383(2): 120-8.
[40]
Elrahman SMA, Abraham A. A review of class imbalance problem. J Net Innov Comput 2013; 2013(1): 332-40.

Rights & Permissions Print Cite
Article Metrics
23
1
© 2024 Bentham Science Publishers | Privacy Policy