Ricerc@Sapienza

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.
METHODS: For this single-center retrospective study we will enroll a cohort of 120 patients admitted to Emergency Department of Sant'Andrea Hospital in Rome, from March 8, 2020 to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, that we will be retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. We will expect to include 60 Patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 Patients with negative COVID-19 RT-PCR by retrieving CT images from picture archiving communication system (PACS). CT Texture analysis (CTTA) will be 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, Kurtosis. Non-parametric Mann Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria will be obtained from Receiver Operating Characteristic (ROC) curves.
EXPECTED RESULTS: We expect to obtain from CTTA analysis differences among texture features extracted that might accurately differentiate COVID-19 pneumonia from pneumonia of other etiology with a high specificity and sensibility.
EXPECTED CONCLUSIONS: CTTA texture analysis will support radiologists in a clear differentiation on chest CT imaging between COVID-19 pneumonia and pneumonia of other aetiology, with a higher sensibility and specificity.