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Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

Caruso, D; Pucciarelli, F; Zerunian, M; Ganeshan, B; De Santis, D; Polici, M; Rucci, C; ... Laghi, A; + view all (2021) Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. La radiologia medica 10.1007/s11547-021-01402-3. Green open access

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Abstract

Purpose To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. Materials and methods One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. Results Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). Conclusions Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

Type: Article
Title: Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11547-021-01402-3
Publisher version: https://doi.org/10.1007/s11547-021-01402-3
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology, Life Sciences & Biomedicine, Radiology, Nuclear Medicine & Medical Imaging, COVID-19, Texture analysis, Diagnostic tool, Computed tomography, CELL LUNG-CANCER, COMPUTED-TOMOGRAPHY, TUMOR HETEROGENEITY, POTENTIAL MARKER, SURVIVAL, DIAGNOSIS, FIBROSIS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging
URI: https://discovery.ucl.ac.uk/id/eprint/10133452
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