tetano
Editor, Senior Moderator
Radiol Med
. 2021 Aug 4.
doi: 10.1007/s11547-021-01402-3. Online ahead of print.
Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia
Damiano Caruso[SUP] 1 [/SUP], Francesco Pucciarelli[SUP] 1 [/SUP], Marta Zerunian[SUP] 1 [/SUP], Balaji Ganeshan[SUP] 2 [/SUP], Domenico De Santis[SUP] 1 [/SUP], Michela Polici[SUP] 1 [/SUP], Carlotta Rucci[SUP] 1 [/SUP], Tiziano Polidori[SUP] 1 [/SUP], Gisella Guido[SUP] 1 [/SUP], Benedetta Bracci[SUP] 1 [/SUP], Antonella Benvenga[SUP] 1 [/SUP], Luca Barbato[SUP] 1 [/SUP], Andrea Laghi[SUP] 3 [/SUP]
Affiliations
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.
Keywords: COVID-19; Computed tomography; Diagnostic tool; Texture analysis.
. 2021 Aug 4.
doi: 10.1007/s11547-021-01402-3. Online ahead of print.
Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia
Damiano Caruso[SUP] 1 [/SUP], Francesco Pucciarelli[SUP] 1 [/SUP], Marta Zerunian[SUP] 1 [/SUP], Balaji Ganeshan[SUP] 2 [/SUP], Domenico De Santis[SUP] 1 [/SUP], Michela Polici[SUP] 1 [/SUP], Carlotta Rucci[SUP] 1 [/SUP], Tiziano Polidori[SUP] 1 [/SUP], Gisella Guido[SUP] 1 [/SUP], Benedetta Bracci[SUP] 1 [/SUP], Antonella Benvenga[SUP] 1 [/SUP], Luca Barbato[SUP] 1 [/SUP], Andrea Laghi[SUP] 3 [/SUP]
Affiliations
- PMID: 34347270
- DOI: 10.1007/s11547-021-01402-3
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.
Keywords: COVID-19; Computed tomography; Diagnostic tool; Texture analysis.