Abstract
ObjectivesThis study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting.MethodsWe identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively.ResultsThe non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease.ConclusionsThe statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis.
Highlights
As of January 5, 2021, there have been over 86 million cases of coronavirus disease 2019 (COVID-19), with more than 1.8 million deaths, worldwide [1]
The non-COVID-19 cohort consisted of 70 patients with 70 computed tomography (CT) scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans
The mean Hounsfield Unit (HU) and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases
Summary
As of January 5, 2021, there have been over 86 million cases of coronavirus disease 2019 (COVID-19), with more than 1.8 million deaths, worldwide [1]. COVID-19 is a disease secondary to infection from severe acute respiratory syndrome coronavirus 2 that can cause pneumonia and lung damage in symptomatic cases. The effect of COVID-19 on the lungs is visible in chest computed tomography (CT) scans. Since radiation oncology patients receive numerous CT scans for their diagnostic work-up, treatment planning, and tumor positioning during treatment, these scans could be leveraged to implement an automated screening tool. Patients receiving cancer treatment are more vulnerable to COVID-19 and have a higher incidence of severe cases and mortality [2,3]. Respiratory illnesses, hypertension, and diabetes have been identified as risk factors for poor COVID-19 outcomes [4]
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