Abstract

Introduction: The highly contagious Coronavirus Disease 2019 (COVID-19) virus has spread across the globe. For the aberrant changes in the lung parenchyma, thin-section chest Computed Tomography (CT) is more sensitive than chest radiography and can identify early disease. Increased CT usage and resolution also lead to an increase in the discovery of "incidental" findings, creating questions about additional research, diagnosis, and follow-up with associated financial and emotional costs. Aim: To evaluate the non COVID-19 lung pathologies and other system findings in High Resolution Computed Tomography (HRCT) chest done for COVID-19 screening study. Materials and Methods: This was a cross-sectional study done in the Department of Radiodiagnosis at Dr.Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation, Vijayawada, Andhra Pradesh, India, from May 2021 to December 2021. The CT scan was done in a craniocaudal direction with an average exposure time of 4-6 seconds, slice thickness of 5 mm and 1.5 mm in mediastinal and lung windows. A total of 1000 HRCT scans done were examined for covid screening. Data was entered in Microsoft Excel and analysed by using Statistical Package for Social Sciences (SPSS) software. Descriptive statistics were represented with percentages. Results: A total of 348 cases were found with various other pathologies incidentally. Lesions like infectious and inflammatory lesions, metabolic diseases, tumours, metastases of various systems and fractures were found. Among 348 total cases with incidental findings 98 cases (28.1%) showed non COVID-19 lung pathologies, 114 cases (32.7%) had findings in the upper abdomen, 30 cases (8.6%) in the thyroid, and three cases (0.8%) had other findings. Conclusion: The HRCT chest screening for COVID-19 not only provided the status of pulmonary infection but gave a broad view of many other system pathologies like in upper abdomen, musculoskeletal system, thyroid, breast which were found incidentally. Identifying those pathologies and correlating them with history will help in early detection and management.

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